Most security leaders aren’t asking for “more DLP.” They’re asking why the DLP they already own is noisy, brittle, and still misses real risk. You turn on endpoint, email, and network DLP. You import PCI and PII templates. Within weeks, users complain that normal work is blocked, so policies get relaxed or disabled. Analysts drown in meaningless alerts. Meanwhile, you know there are blind spots in SaaS, cloud data stores, and AI tools that DLP never sees.

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The problem usually isn’t that you bought the “wrong” DLP. It’s that DLP is doing too much on its own: trying to discover sensitive data, understand business context, and enforce policies in one step. To improve the functioning of your DLP, you have to separate those responsibilities and give DLP the data intelligence it has always been missing.

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This guide walks through seven data loss prevention best practices that:

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• Cut DLP false positives and alert fatigue
• Close blind spots across SaaS, cloud, and AI
• Show how to use Data Security Posture Management (DSPM) alongside DLP instead of treating them as competitors

1. Start with a specific DLP problem, not a vague mandate

Many DLP programs are born from a broad requirement like “prevent data loss” or “achieve compliance.” That sounds reasonable, but it’s too fuzzy to drive design decisions. If everything is “data loss,” every event looks important and tuning turns into guesswork. Instead, define one or two sharp, testable problems to solve in the next 90 days.

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For example:

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• Reduce DLP false positives by 50% while maintaining coverage across email and collaboration tools.
• Eliminate unknown PHI exposures in Microsoft 365 and Google Workspace before the next HIPAA audit.
• Stop real customer data from leaking into lower environments and AI training pipelines.

Once you frame the goal concretely, a few things fall into place. You know what to measure (false-positive rate, blind-spot coverage, number of mis‑labeled data stores). You can see which parts are posture problems (where data lives, how it’s labeled, who can touch it) and which are pure enforcement. And you have a clear way to tell whether the program is actually improving, rather than just “having DLP turned on.” In short, give your DLP initiative a narrow, measurable purpose before you touch any rules.

2. Fix classification before you tune DLP rules

Almost every struggling DLP deployment eventually discovers the same truth: it doesn’t really have a DLP problem, it has a classification problem. Traditional DLP leans heavily on pattern matching and static dictionaries. In modern environments, that leads to constant mistakes:

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• Internal IDs or ticket numbers mistaken for card data or SSNs
• Highly sensitive business documents missed because they don’t match canned patterns
• Each product (endpoint DLP, email DLP, CASB) trying to re‑implement classification in its own silo

This is exactly the gap DSPM is designed to fill. A platform like Sentra DSPM continuously:

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• Discovers sensitive data at scale across cloud, SaaS, data warehouses, on‑prem stores, and AI pipelines, without copying it out of your environment
• Classifies that data using multi‑signal, AI‑driven models that combine entity‑level signals (PII, PCI, PHI fields, secrets) with file‑level semantics (document type, business function, domain)
• Labels assets consistently, for example, by auto‑applying Microsoft Purview Information Protection (MPIP) labels that downstream tools, including DLP, can consume

Once you trust the labels, DLP can stop trying to “guess” sensitivity from raw content and location. Policies get simpler and more stable because they key off well‑defined labels instead of brittle regular expressions.

Best practice: before you tweak another DLP rule, invest in getting classification right with DSPM, then let DLP enforce on the resulting labels.

3. Reduce DLP false positives with labels and context

“Reduce DLP false positives” is one of the most common reasons security teams revisit their DLP strategy. Most false positives come from two root causes:

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• Over‑broad content rules that match anything vaguely sensitive
• Lack of business context like; who the user is, which system they’re in, where the data is going, and whether that’s normal behavior

The first step is to move to label‑driven policies wherever possible. Instead of “block anything that looks like a credit card number,” write rules like “block sending files labeled PCI to personal email domains” or “quarantine emails with PHI labels sent outside approved partners.” DSPM plus accurate labeling makes that possible at scale.

The second step is to bring in more context. A file labeled Confidential going to a known external auditor is very different from that same file going to a new personal Dropbox account at 2 a.m.

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When you combine labels with:

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• Identity and role
• Channel (email, web, SaaS, AI)
• Destination and geography
• Simple behavior analytics (volume, unusual time, unusual location)

You can reserve hard blocks and escalations for situations that actually look risky.

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Finally, you need a real feedback loop. Let users override certain DLP prompts with a required justification and log “reported false positives.” Review those regularly with business owners. That feedback is invaluable for tightening rules where they truly matter and relaxing them where they are just creating friction. In practice, enforce on labels first, then refine with business context and user feedback, instead of trying to make regexes infinitely smarter.

4. Treat DSPM and DLP as a single system, not a “DSPM vs DLP” choice

If you search for “DSPM vs DLP,” you’ll find plenty of comparison articles and vendor takes. From the customer’s side, though, the most useful framing is not “which one?” but “what does each do, and how do they work together?”

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At a high level:

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• DSPM focuses on data-at-rest intelligence: it shows what sensitive data you have, where it resides, who and what can access it, how it’s configured, and whether that posture is acceptable for your risk and compliance requirements.
• DLP focuses on data-in-motion enforcement: it monitors data leaving (or moving within) the organization via email, endpoints, web, SaaS, and APIs, and decides what to block, encrypt, or just log based on policies.

When you connect them, you get a closed loop:

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1. DSPM discovers, classifies, and labels sensitive data consistently across cloud, SaaS, on‑prem, and AI.
2. Data access governance uses that context to right‑size permissions and remediate over‑exposure.
3. DLP and related controls enforce label‑driven policies at the edges, with far fewer false positives and blind spots.

DSPM doesn’t replace DLP; it makes DLP accurate, scalable, and cloud/AI‑ready. Takeaway, stop framing it as DSPM versus DLP. Your DLP will only be as good as the DSPM feeding it.

5. Bring SaaS, cloud, and AI into scope for DLP

Most older DLP programs were built around email and endpoints. But in cloud‑first organizations, the riskiest data flows now run through:

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• Cloud and object storage (S3, GCS, Azure Blob)
• Data warehouses and lakes (Snowflake, BigQuery, Databricks)
• SaaS platforms (M365, Google Workspace, Box, Salesforce, Slack, Teams)
• AI systems (M365 Copilot, Gemini for GWS, Bedrock, custom RAG apps)

Trying to bolt classic inline DLP controls onto all of those surfaces is expensive and incomplete. You’ll still miss shadow data, lower environments that contain real customer data, and AI pipelines that consume sensitive content by design.

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DSPM gives you a more scalable pattern:

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• Inventory and classify sensitive data where it sits across cloud, SaaS, and AI.
• Use that intelligence to drive native controls: MPIP labels and Microsoft Purview DLP, CASB/SSE policies, Snowflake dynamic masking, IAM/CIEM, and AI guardrails.

For example, a healthcare organization might combine:

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• Sentra’s DSPM to discover PHI in Google Drive, M365, Salesforce, and Snowflake
• Auto‑labeling of that PHI so Purview and DLP can enforce correctly
• AI‑aware classification to govern which labeled data copilots and agents are allowed to see

See How Valenz Health Uses DSPM to Protect PHI Across AWS, Azure, and Modern Data Platforms

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Similarly, the DLP for Google Workspace story shows how cloud‑native, DSPM‑powered classification is essential to make platform DLP effective for unstructured content in OneDrive, SharePoint, and Teams. Best practice, treat SaaS, cloud, and AI as first‑class DLP surfaces, and use DSPM to make them visible and governable before you try to enforce.

6. Design DLP policies for real workflows, then harden them

Many DLP programs fail not because the tools are weak, but because the policies were designed for whiteboards, not for real users.

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Very often:

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• The ruleset is too broad, with dozens of overlapping controls per channel
• Business stakeholders had little input, so workflows break in production
• There’s no staged rollout path; policies jump straight from “off” to “block”

A better pattern is to treat DLP policies as something you product‑manage. Start by expressing a very small set of core policies in business terms, independent of channel.

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For example:

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• “Regulated data (PII, PCI, PHI) must not leave specific regions or approved partners.”
• “Files labeled Highly Confidential must never be shared to personal email or cloud domains.”
• “AI assistants and copilots may only access data labeled Internal or below.”

Then map those policies onto channels with graduated responses:

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• Log only (for simulation and tuning)
• User prompts (“This file is labeled Confidential; are you sure?”)
• Override with justification (captured for review)
• Hard block + ticket for the riskiest conditions

Throughout, involve legal, compliance, HR, and business owners. If DLP events could lead to performance conversations or disciplinary action, you don’t want those stakeholders to be surprised by how the system behaves.

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Ready to get started? Read: How to Build a Modern DLP Strategy That Actually Works: DSPM + Endpoint + Cloud DLP

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Key idea, roll out label‑driven policies gently, let reality teach you where controls can be strict, and only then lock them down.

7. Measure DLP like a product, not a checkbox

If your goal is to “supercharge DLP so it performs better,” you need to know how it’s performing now, and how changes affect it. That means treating DLP like a product with KPIs, not a compliance box you either have or don’t.

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High‑performing teams tend to track four categories:

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• Coverage: percentage of data stores under DSPM visibility; proportion of sensitive assets correctly labeled; number of major SaaS and cloud platforms within scope.
• Quality: false positive and false negative rates by policy and channel; serious incidents discovered outside DLP that should have triggered it.
• Operational impact: mean time to detect and respond to data‑loss incidents; analyst hours spent per week on DLP triage; number of issues auto‑remediated via workflows (auto‑labeling, auto‑revoking access, auto‑quarantining content).
• Business alignment: frequency of stakeholder requests to disable or bypass policies; time to prepare for audits compared to prior years.

A platform like Sentra’s data security platform gives you much of this telemetry out of the box through its unified inventory, access graph, and integration hooks into SIEM/SOAR, IAM, DLP, SSE/CASB, and ITSM. Bottom line, you can’t fix what you can’t measure. Decide which DLP metrics matter to your organization and revisit them as you evolve your DSPM + DLP architecture.

What “Supercharge Your DLP” means in practice

When teams say “we need to fix our DLP,” they usually don’t mean “rip everything out.” They mean:

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• “We don’t trust the alerts we get.”
• “We know there are blind spots in cloud, SaaS, and AI.”
• “We’re tired of fighting with brittle rules that don’t reflect how the business actually works.”

Supercharging DLP in the cloud and AI era starts with data intelligence. That means:

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• Using DSPM to discover and classify sensitive data everywhere
• Applying consistent labels that encode business meaning
• Wiring those labels into the DLP and access controls you already own

From there, DLP can finally do what it was always meant to do: prevent real data loss, at scale, without paralyzing your organization or your AI initiatives. That’s the real promise behind “Supercharge Your DLP.” You don’t start over, you make the DLP you already have smarter, quieter where it should be, and louder where it counts.

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RSAC 2026 is shaping up to be one of the most important RSA Conferences to date, especially for security teams navigating AI adoption, Copilot readiness, and large-scale data governance. At RSA Conference 2026 in San Francisco, Sentra is bringing together security leaders from major enterprises across financial services and global consumer industries to discuss how modern enterprises are preparing their data for AI, strengthening governance, and rethinking DLP in an AI-driven world.

If you’re attending RSAC 2026, here’s where to find us, and why it matters.

CISO AI Copilot Readiness Roundtables at RSAC 2026

March 23–26 | W Hotel | Steps from Moscone

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AI assistants like Microsoft Copilot and Google Gemini are transforming how employees access enterprise data. What once required manual searches across drives, mailboxes, and SaaS applications can now be surfaced instantly.

That shift is powerful, but it also forces CISOs to confront a difficult question: is our data actually AI-ready?

During RSAC 2026, Sentra is hosting closed-door CISO AI Copilot Readiness Roundtables, bringing together security leaders from major enterprises across financial services and global consumer industries. These sessions are intentionally intimate and designed for candid peer discussion rather than vendor presentations.

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No slides. No marketing decks. Just real-world insights on what’s working, and what isn’t - as organizations operationalize AI securely. Register for a roundtable.

AI Data Readiness for 70+ PB: Lessons from a Leading Financial Platform at RSAC 2026

March 24 | 7:45 AM – 9:00 AM

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Preparing data for AI at scale is not theoretical, especially when you're dealing with more than 70 petabytes of data.

In this RSAC 2026 session, a former Director of Product Security from a leading digital financial platform will share how their organization approached AI data readiness using Sentra. The session will explore how large financial institutions can gain visibility into massive data environments, reduce exposure risk, and enable Copilot and machine learning adoption without compromising governance.

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If you're managing AI adoption in a complex, high-scale environment, this session offers practical lessons grounded in real-world enterprise execution. Register for the session.

Continuous Compliance with AI Visibility: Lessons from a Major Mortgage Institution at RSAC 2026

March 25 | 12:00 PM – 1:00 PM

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For a $500B U.S. mortgage institution, compliance is not a one-time event, it’s a continuous obligation.

In this RSA Conference 2026 session, a CISO from one of the largest mortgage lenders in the United States will share how their organization uses Sentra to gain visibility into sensitive data, automate Jira masking workflows, and transform compliance from a reactive burden into a proactive advantage.

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As regulatory expectations increase around AI systems and data governance, continuous compliance becomes a strategic capability rather than just an audit checkbox. Register for the session.

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A Global Enterprise Blueprint for Modern DLP Compliance at RSAC 2026

Global enterprises face an even more complex challenge: governing data consistently across Azure, Snowflake, Microsoft 365, and Purview, while preparing for AI and Copilot integration. At RSAC 2026, data security leaders from one of the world’s largest consumer brands will share how they built a governance framework that integrates large data catalogs with modern DLP controls. The session explores how traditional policy-based DLP can evolve into a model that combines deep data intelligence with enforcement aligned to business context.

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For organizations operating across regions and platforms, this blueprint offers a practical path forward. Register for the session.

Visit Sentra at Booth #N4607 at RSA Conference 2026

If you’re walking the floor at RSAC 2026, stop by Booth N4607 to explore how Sentra enables AI-ready data security.

Our team will be showcasing how organizations can:

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• Eliminate risk from AI agents and ML model adoption
  
• Discover unknown sensitive data exposures
  
• Add AI-powered intelligence to improve DLP precision
  

Rather than simply layering new policies on top of old systems, we’ll demonstrate how DSPM and DLP can work together in a unified architecture. Book a Demo at Booth N4607.

Executive Briefings at RSAC 2026

For security leaders looking to go deeper, Sentra is offering private briefings during RSA Conference 2026. These sessions provide the opportunity to discuss real-world data security challenges, proven best practices, and lessons learned from enterprise deployments.

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Each discussion is tailored to your environment, whether your focus is AI readiness, exposure reduction, or continuous compliance. Schedule a Personal Briefing.

Special Events During RSAC 2026

The Women in Security Documentary

March 24 & 25 | AMC Metreon 16

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Just steps from Moscone Center, join us for a special screening celebrating women redefining leadership in cybersecurity. The red carpet begins at 4:00 PM, with the screening starting at 4:45 PM.

Register Now

Sentra + Defensive Networks RSA Dinner

March 25 | 7:00 PM | The Tavern, San Francisco

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We’re hosting an intimate, relationship-centered dinner for security leaders navigating today’s most pressing AI and data security challenges. Designed for meaningful dialogue and peer exchange, this event offers space for authentic conversation beyond the conference floor.

Why AI Data Security Defines RSAC 2026

The defining theme of RSA Conference 2026 is clear: AI has changed the security equation. AI systems do not create new data, but they dramatically increase its discoverability, accessibility, and movement. That reality exposes gaps between visibility and enforcement that many organizations have tolerated for years. To secure AI adoption, organizations need more than isolated tools. They need continuous data intelligence, context-aware enforcement, and feedback between the two. That is the architecture Sentra is bringing to RSAC 2026.

See You at RSA Conference 2026

If you’re attending RSAC 2026 in San Francisco, we’d love to connect.

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📍 Booth N4607
📅 March 23–26, 2026
📍 Moscone Center

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Join us to explore how AI-ready data security becomes practical, measurable, and operational- not just theoretical.

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When I talk to CISOs and privacy leaders in Florida, the conversation usually starts the same way:

“We know we should be better prepared for a breach. But the 30‑day deadline under FIPA… that’s what keeps us up at night.”

I get it. On paper, Florida’s Information Protection Act of 2014 (FIPA), codified in Florida Statutes § 501.171, is just another notification law. In real life, that 30‑day requirement to notify affected Floridians (and sometimes the Attorney General and credit bureaus) collides with the messy reality of cloud data sprawl, legacy systems, and half‑documented SaaS.

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In this post, I want to walk through FIPA the way I explain it in one‑on‑one conversations:

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• What FIPA actually says, in plain language
• Why the 30‑day breach clock is so unforgiving
• The patterns I see in Florida across healthcare, insurance, and travel/hospitality
• How a data‑centric approach and DSPM specifically changes the game

I’m not your lawyer (you should definitely loop them in), but I am someone who spends a lot of time working with Florida‑based teams trying to operationalize this law.

What FIPA actually requires (without the legalese)

FIPA was passed to “better protect Floridians’ personal information” and to force businesses and government entities to do two big things:

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1. Take reasonable measures to protect personal information
2. Notify people quickly when something goes wrong

The law lives in § 501.171 of the Florida Statutes. The core ideas are:

• If you’re a covered entity (a business or government entity that “acquires, maintains, stores, or uses” personal information), you have to secure that data and follow FIPA’s rules when there’s a breach.
• If you experience a breach involving Florida residents’ personal information, you usually have to notify them within 30 days of determining a breach occurred, with a narrow option for a 15‑day extension if you can show good cause to the Attorney General.
• If 500 or more Florida residents are affected, you also have to notify the Florida Attorney General within that same 30‑day window.
• If more than 1,000 residents are affected, you must notify the nationwide credit reporting agencies (think Equifax, Experian, TransUnion) as well.

On top of that, FIPA imposes:

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• Data security obligations: “reasonable measures” to protect and secure personal information in electronic form.
• Disposal requirements: you must take reasonable measures to dispose of customer records containing personal information when no longer needed; shredding, erasing, or otherwise making the data unreadable.
• Civil penalties for failure to notify, up to $500,000 per breach depending on how long you delay.

The Florida Attorney General’s own guidance makes the intent clear: FIPA isn’t just about writing a nice policy; it’s about timely, meaningful transparency when Floridians’ data is at risk.

What “personal information” means under FIPA

One thing that trips teams up is how broad Florida’s definition of “personal information” really is.

Under § 501.171, personal information generally means a Florida resident’s first name or first initial and last name in combination with one or more of these data elements, when not encrypted:

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• Social Security number
• Driver’s license, ID card, passport, military ID, or similar government identifier
• Financial account number, credit or debit card number plus any required code, PIN, or password needed to access the account
• Information about a person’s medical history, mental or physical condition, or medical treatment or diagnosis by a healthcare professional
• Health insurance policy numbers, subscriber IDs, or unique identifiers used by a health insurer
• A username or email address combined with a password or security question/answer that would permit access to an online account

So if you’re in Florida healthcare, insurance, banking, or even e‑commerce, FIPA isn’t just about raw SSNs. It picks up:

• Patient portal credentials
• Online banking logins
• Health plan IDs
• Medical billing data

And it doesn’t stop there: the University of Florida’s privacy office, for example, explicitly points out that FIPA’s definition covers both medical and financial identifiers, plus account credentials.

This matters, because it means you can’t treat “regulated data” as just PHI or PCI. FIPA cares about all of those elements.

What counts as a “breach” and when the 30‑day clock starts

FIPA defines a “breach of security” (or “breach”) as unauthorized access of data in electronic form containing personal information.

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A few important nuances I always emphasize:

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• The access has to be unauthorized. Good‑faith access by an employee or agent for legitimate business purposes isn’t a breach as long as the data isn’t misused or further disclosed.
• The data in question has to contain personal information as Florida defines it—so you need to know what’s actually stored where.
• Encrypted data generally doesn’t trigger a breach unless the encryption keys or methods themselves are compromised.

The 30‑day notification deadline doesn’t start the moment your EDR fires an alert. It starts when you “determine that a breach has occurred” or have reason to believe it has.

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And this is where reality bites:

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• To “determine that a breach occurred,” you have to scope the incident: what system, what data, which individuals, what type of information.
• The Attorney General and courts will absolutely look at whether you dragged your feet on that determination. FIPA allows a short extension (15 days) if you show good cause in writing, but it doesn’t give you months to figure things out.

I’ve yet to meet a Florida CISO who feels like 30 days is generous. For most, it’s barely enough time if they don’t have good visibility going in.

What notice actually looks like in Florida

Once you’ve determined you have a FIPA breach, here’s what notice looks like in practice.

Notice to individuals

You must notify each affected Florida resident as expeditiously as possible and without unreasonable delay, but no later than 30 days after you determine a breach occurred (unless law enforcement asks you to delay, or you get that 15‑day AG extension).

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The notice has to include at least:

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• The date or estimated date range of the breach
• A description of the personal information that was accessed
• Contact information for your organization so people can ask questions or get help

You can send notice by mail or email, depending on how you normally communicate with that person, with substitute notice (website + media) allowed when certain cost or scale thresholds are met.

Notice to the Attorney General

If 500 or more Florida residents are affected, you must also notify the Florida Attorney General’s Office within that same 30‑day window.

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That notice must include:

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• A synopsis of the events
• The number of affected residents
• Any services you’re offering (like credit monitoring)
• A copy of what you sent to consumers
• Contact information for someone at your organization who can answer follow‑up questions

And if the AG asks, you also need to be able to provide things like police or incident reports, your internal breach policies, and the steps you’ve taken to fix the problem.

Notice to credit bureaus

If more than 1,000 individuals are notified, you must also notify all nationwide consumer reporting agencies about the timing, distribution, and content of the notice.

Why this is so hard for Florida organizations in 2026

Most of the teams I work with in Florida aren’t struggling because they don’t care about FIPA. They’re struggling because, when something bad happens, they can’t answer three basic questions fast enough:

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1. What data was actually in the affected systems?
   
   • Was it just emails and low‑risk metadata?
   • Or did that S3 bucket / SQL database / M365 site hold SSNs, health data, insurance IDs, or account credentials for Florida residents?
     
2. How many Floridians are actually impacted?
   
   • Do we have 73 residents involved, or 73,000?
   • Can we reliably separate Florida addresses from the rest of the world for notification purposes?
3. Was the data really “unsecured”?
   
   • Was it properly encrypted with keys stored separately?
   • Do we have logs that show whether an attacker actually exfiltrated data, or just probed the perimeter?

The 30‑day clock feels brutal because you’re trying to do all of that from a cold start. Digging through logs, reconstructing schemas, pulling sample rows, manually joining data to geography, arguing about what “personal information” means asset by asset.

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I see this especially clearly in Florida’s core industries:

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• Healthcare teams trying to line up FIPA with HIPAA’s 60‑day breach rule and HHS obligations.
• Insurers and health plans juggling FIPA alongside sector‑specific regulations and contractual obligations.
• Travel and hospitality brands sitting on huge volumes of guest data; IDs, payment details, loyalty credentials. All of which can qualify as personal information under FIPA.

When you already have patchy visibility, the law’s timeline just exposes that weakness and creates crushing pressure for security, privacy, and GRC teams.

How a data‑centric approach and DSPM change the equation

This is why I keep coming back to data‑centric security and Data Security Posture Management (DSPM) in conversations about FIPA.

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Instead of starting each incident from zero, a DSPM platform like Sentra gives you an always‑on, high‑accuracy answer to:

• What sensitive data do we have?
• Where does it live (down to specific buckets, tables, and documents)?
• How sensitive is it, based on FIPA, HIPAA, PCI, and other regimes?
• Who can actually access it; including users, service accounts, and AI tools?

That changes the FIPA conversation in a few ways:

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• Before an incident, you can see where Florida‑defined “personal information” has ended up—especially in cloud storage, data lakes, and collaboration tools—and fix obvious exposures (like unencrypted data or over‑permissioned access) long before someone breaks in.
• During an incident, you’re not guessing which assets in the blast radius actually contain personal information; you already know. That lets you scope affected systems and residents much faster.
• After an incident, you have a defensible record of what you did, why you did it, and how you’re preventing a repeat. This is exactly what the AG and auditors tend to ask for.

And because DSPM is agentless and API‑driven, you don’t have to slow your developers down with heavy‑weight deployments. It fits into the cloud‑native world most Florida organizations already live in.

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If you’re curious how this looks in a highly regulated, fast‑moving environment, the SoFi DSPM story with Sentra is a good parallel, even though it’s financial services, not Florida healthcare or hospitality. They had to solve the same problems: data sprawl, regulatory pressure, and the need to move quickly without losing control.

A FIPA‑ready checklist I walk through with Florida teams

When I’m sitting with a Florida customer and FIPA is on the agenda, we usually work through some version of this:

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1. Do we really know where FIPA‑defined personal information lives across our environment?
   Not just in the EHR, policy admin system, or booking engine, but in data lakes, backup buckets, BI tools, and SaaS.
   
   
2. Can we tell, with confidence, how many Florida residents are in those datasets?
   If an S3 bucket in us‑east‑1 is compromised, can we quickly identify the Florida slice?
   
   
3. Do we have a FIPA‑aware incident playbook?
   One that explicitly calls for:
   
   • Pulling DSPM data to identify affected systems and data types
   • Running a structured risk assessment around “breach of security”
   • Triggering the right notices (residents, AG ≥500, CRAs ≥1,000) inside 30 days
     
     
4. Are we shrinking our FIPA exposure over time?
   Are we cleaning up old copies, tightening access, and encrypting the right things?
   

When those answers are “yes,” the 30‑day clock feels a lot less like a panic button, and a lot more like a tight but manageable SLA.

Final thought (and a practical next step)

FIPA isn’t going away. If anything, the broader trend in Florida is toward more privacy and security scrutiny, not less.

My honest view, after a lot of conversations in this state, is that the only sustainable way to live with that 30‑day breach deadline is to stop treating data security as an abstract perimeter problem and start treating it as a continuous, data‑centric discipline. That’s exactly what Sentra’s DSPM platform is built for.

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If this resonates and you’re looking at FIPA wondering how you’d really perform under a 30‑day clock, let’s make it concrete.

See how Sentra can show you exactly where FIPA‑defined personal information lives today, what’s exposed, and how to cut your breach‑response time from weeks to days. Request a Sentra demo.

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Every enterprise security team knows how to scan documents, spreadsheets, and databases for sensitive data. But what about the thousands of call recordings sitting on your file servers? The Zoom meetings archived in cloud storage? The voicemails accumulating in your communications infrastructure?

Audio and video files represent the fastest-growing category of unstructured data in the enterprise, and for most organizations, they remain completely invisible to data security programs. That gap is not just an oversight. It is a liability.

The Explosion of Audio and Video Data

The modern enterprise generates an extraordinary volume of audio and video content. Customer service centers record every call. Sales teams capture prospect conversations. HR departments archive interview recordings. Legal teams store depositions and witness interviews. And since the shift to hybrid work, nearly every meeting produces a recording.

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This content is rich with sensitive information. A single customer service call might include a spoken Social Security number, a credit card number read aloud for verification, an account number, and a full name and address. A recorded executive meeting might contain confidential M&A discussions, unreleased financial results, or strategic plans that constitute material nonpublic information. A telehealth session captures protected health information that falls squarely under HIPAA.

Yet the vast majority of Data Loss Prevention (DLP) and Data Security Posture Management (DSPM) solutions simply skip these files. They were built for text. They parse documents, scan databases, and index emails, but when they encounter an MP4 or a WAV file, they move on. The result is a massive blind spot that grows larger every quarter.

How Sentra Scans Audio and Video at Scale

Sentra closes this gap with purpose-built audio and video scanning capabilities that bring the same depth of sensitive data discovery to media files as organizations already expect for documents and databases.

Broad Format Coverage

Sentra supports more than 20 audio formats — including MP3, WAV, FLAC, AAC, OGG, OPUS, WMA, M4A, AIFF, AMR, APE, AU, CAF, DTS, AC3, ALAC, PCM, WV, RA, SDP, and many more — along with 15+ video formats such as MP4, MKV, AVI, MOV, WebM, FLV, WMV, MPG/MPEG, 3GP/3G2, VOB, ASF, MXF, OGV, M4V, and F4V. This is not a narrow proof of concept limited to a handful of common codecs. It is production-grade coverage designed for the diversity of formats found in real enterprise environments.

ML-Powered Transcription and Extraction

At the core of Sentra's media scanning is a dedicated ML server that performs audio transcription using advanced machine learning models. For video files, Sentra automatically extracts the audio track and routes it through the same transcription pipeline. The transcribed text then flows into Sentra's full classification and extraction engine, where it is analyzed against hundreds of data classifiers to identify PII, financial data, healthcare information, credentials, and other sensitive content.

This entire process runs inside your cloud environment, using streaming-based processing that avoids sending media files to Sentra’s SaaS and minimizes any persistence of sensitive audio.

Where This Matters Most

Financial Services

Regulatory requirements in financial services make audio scanning not just useful, but essential. MiFID II mandates the recording and monitoring of communications related to client orders, including voice calls. Dodd-Frank imposes similar requirements on swap dealers and major swap participants. SEC and FINRA recordkeeping rules require broker-dealers to retain and supervise communications, and those rules have expanded to cover a widening range of channels.

Trading floor recordings, client advisory calls, and internal communications all potentially contain material nonpublic information, account details, and transaction data. Without the ability to scan this content, compliance teams are operating with an incomplete picture of where sensitive data lives.

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Healthcare

Telehealth has moved from a pandemic stopgap to a permanent fixture of care delivery. Every virtual appointment generates a recording that may contain diagnoses, treatment plans, medication names, patient identifiers, and insurance details — all of which constitute protected health information under HIPAA. Healthcare organizations that scan their document repositories but ignore their telehealth archives are leaving a significant compliance gap unaddressed.

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Legal

Law firms and corporate legal departments handle some of the most sensitive information in any organization. Deposition recordings, witness interviews, settlement discussions, and privileged attorney–client conversations are routinely captured as audio or video files. A single misplaced recording can constitute a privilege waiver or a data breach. Knowing exactly what sensitive content these files contain is a prerequisite for proper data governance.

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Customer Service and Sales

Contact centers are among the largest producers of audio data in any enterprise. Every recorded call is a potential repository of customer PII - names, addresses, phone numbers, account numbers, and payment card data spoken aloud during verification procedures. Organizations subject to PCI DSS have a particular obligation to understand where cardholder data exists, and that includes call recordings where a customer reads their card number to an agent.

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Corporate Communications

The post-pandemic workplace runs on recorded meetings. Zoom, Microsoft Teams, and Google Meet archives grow continuously, containing everything from routine standups to board-level strategy sessions. These recordings may capture discussions about personnel matters, financial performance, product roadmaps, and partnership negotiations. They are a rich and largely unmonitored source of sensitive data exposure.

Closing the Last Major Gap in Data Discovery

Most organizations have invested heavily in scanning their structured and semi-structured data stores. They have cataloged their databases, indexed their document repositories, and classified their cloud storage. But the audio and video content accumulating across their infrastructure remains a blind spot - not because it is unimportant, but because the tooling to scan it simply did not exist at enterprise scale.

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Sentra changes that equation. By extending the same rigorous data discovery and classification capabilities to dozens of audio and video formats, with ML-powered transcription running inside your cloud environment, Sentra enables security and compliance teams to achieve genuine visibility into their complete data estate. The sensitive data in your call recordings, meeting archives, and video files is not going away. If anything, the volume is accelerating. The question is whether your data security program can see it.

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The recent Microsoft Copilot Chat incident, in which enterprise users reportedly saw AI-generated summaries that included confidential content from Drafts and Sent Items despite sensitivity labels and DLP policies, has reignited a critical conversation about AI assistant security.

Microsoft clarified that Copilot did not bypass underlying access controls. But that explanation only addresses part of the problem. The real issue isn’t whether Microsoft Copilot broke security controls. It's that Copilot inherits user permissions, and can apply its extensive abilities to uncover data the user may have long forgotten (or never properly secured in the first place).

Copilot didn’t create new risks, it surfaced existing exposure - instantly, at scale, and in a way that made it visible. For organizations deploying Microsoft Copilot, that distinction matters.

Why the Microsoft Copilot Incident Matters More Than It Appears

Microsoft Copilot operates within the permissions of the signed-in user. On paper, that sounds safe. In reality, it means Copilot can access everything the user can access - across years of accumulated data.

In a typical Microsoft 365 environment, that includes:

• Emails stretching back years
• Linked SharePoint Online documents
• OneDrive folders shared broadly across teams
• External guest-accessible sites
• Archived projects no one has reviewed in years

When Copilot summarizes a mailbox, it can follow embedded links into SharePoint and OneDrive. If those linked files contain overshared financials, HR investigations, contracts, or regulated data, Copilot can surface insights from them in seconds.

Previously, this data exposure existed quietly in the background. AI assistants remove friction:

• No need to manually search multiple systems
• No need to remember file locations
• No need to understand organizational silos

A single natural-language prompt can traverse it all.

That is the shift. And that is the risk.

AI Assistants Change the Data Risk Model

Traditional enterprise security assumes that risk is constrained by human effort. Data may technically be accessible, but if it requires time, institutional knowledge, or manual searching, exposure is limited.

AI assistants like Microsoft Copilot eliminate those barriers.

Instead of asking, “Who has access to this file?” organizations must now ask:

“What can an AI assistant synthesize from everything a user can access?”

This is a fundamentally different security model.

The Microsoft Copilot Chat incident demonstrated that even when sensitivity labels and DLP policies are in place, unexpected AI-generated outputs can undermine confidence. The concern is not only regulatory exposure, its reputational, operational, and executive trust in AI initiatives.

Why Sensitivity Labels and DLP Are Not Sufficient for Copilot Security

Many organizations rely on Microsoft Purview, sensitivity labels, and Data Loss Prevention (DLP) policies to control how information is handled in Microsoft 365.

Those tools are essential, but they are not enough on their own.

In real-world environments:

• Labels are inconsistently applied
• Legacy data predates modern classification policies
• SharePoint sites remain broadly accessible long after projects end
• OneDrive folders accumulate stale and redundant files
• Linked documents inherit exposure from misconfigured parent sites

AI assistants operate on access reality, not policy intention. If sensitive data is accessible (even unintentionally) Copilot can surface it. The Copilot Chat incident did not reveal a failure of AI. It revealed a failure of data posture alignment.

Microsoft Copilot Requires AI Data Readiness

Before enabling Copilot broadly across Microsoft 365, organizations need what can be described as AI Data Readiness.

AI Data Readiness means achieving continuous visibility into:

• Where sensitive data lives
• How it is shared internally and externally
• Which SharePoint and OneDrive assets are overshared
• Whether classification matches actual content
• What historical data remains unnecessarily accessible
  
  

Without this foundation, Copilot becomes a force multiplier for hidden exposure.

With it, Copilot becomes a productivity accelerator.

DSPM: The Missing Layer in Secure Microsoft Copilot Deployment

Data Security Posture Management (DSPM) provides the continuous, data-centric visibility required for secure AI adoption.

Rather than focusing solely on permissions or labels, DSPM answers deeper questions:

• What sensitive and regulated data exists across Microsoft 365?
• Where is it exposed?
• What is its purpose? 
• Who can access it?
• How does it move?
• Is it properly classified and governed?

Sentra’s DSPM-driven approach continuously discovers and classifies sensitive data across SharePoint Online, OneDrive, cloud storage, and SaaS platforms. Using AI-enhanced classification, it differentiates routine collaboration documents from high-risk assets such as HR investigations, financial statements, intellectual property, and regulated PII or PHI.

This creates a unified, context-rich map of enterprise data exposure, the exact context Copilot relies on when generating responses.

From Visibility to Remediation

Once visibility exists, security teams can act with precision.

Instead of broadly restricting Copilot access, which reduces productivity, organizations can surgically reduce risk by:

• Identifying overexposed SharePoint sites containing sensitive data
• Detecting OneDrive folders shared with large groups or external guests
• Removing stale, redundant, and “ghost” data
• Reconciling missing or misaligned sensitivity labels
• Aligning MPIP and DLP controls with actual content reality

The result is not AI avoidance. It is controlled AI expansion.

The Strategic Shift: Treat Copilot Security as a Data Problem

The Microsoft Copilot Chat incident should not trigger panic. It should trigger maturity.

AI assistants reflect the state of your data. If your Microsoft 365 environment contains overshared, misclassified, or stale sensitive information, AI will surface it.

Organizations that succeed with Microsoft Copilot will be those that:

• Audit their Microsoft 365 data exposure continuously
• Reduce unnecessary access before enabling AI at scale
• Align labels, policies, and actual content
• Limit AI blast radius through data posture improvements
• Treat AI adoption as a data governance transformation

The conversation should move from “Is Copilot safe?” to:

“Is our data posture ready for Copilot?”

When DSPM underpins AI adoption, Copilot shifts from potential liability to competitive advantage.

Final Thought: AI Assistants Don’t Create Risk - They Reveal It

The Microsoft Copilot incident is not an isolated anomaly. It is an early indicator of how AI assistants will reshape enterprise security assumptions. Copilot can only summarize what users already have access to. If access is overly broad, outdated, or misconfigured, AI will expose that reality faster than any audit ever could.

Organizations that invest in AI Data Readiness today will not only prevent future incidents, they will accelerate secure AI transformation across Microsoft 365.

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DSPM Has a Labor Problem

Every security team knows the cycle: an alert fires, you open a dashboard, click through four screens to understand the context, pivot to a second tool to check who has access, cross-reference a spreadsheet to determine the data's sensitivity, then manually update the alert status. Multiply that by dozens of alerts a day, and your team's most experienced engineers spend more time navigating tools than actually improving security posture.

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The data security industry invested heavily in visibility. We can tell you where your PII lives, which buckets are public, and how many identities can reach your crown jewels. But visibility without action is just a more sophisticated way to worry. The gap between seeing a problem and resolving it remains filled with manual work, context switching, and tribal knowledge locked in senior engineers' heads.

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What if an AI agent could do the navigation, the correlation, and the remediation for you, and you could just tell it what you need in plain English?

What Is MCP, and Why Should Security Teams Care?

The Model Context Protocol (MCP) is an open standard that connects AI assistants like Claude to external tools and data sources. Think of it as a universal adapter: instead of building custom integrations for every AI workflow, MCP provides a standardized way for AI agents to discover and call tools, read data, and execute operations.

For security teams, MCP means you can interact with your entire security platform through natural language. No more memorizing API endpoints, constructing filter syntax, or building one-off scripts. You describe what you need, and the AI agent chains together the right API calls to deliver it.

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But here's the critical distinction: not all MCP servers are created equal.

Some MCP implementations expose a handful of read-only catalog queries that are useful for asking "what data do I have?" but powerless when you need to actually do something about what you find. Read-only MCP servers give you a conversational interface to a dashboard. That's a UX improvement, not a paradigm shift.

Sentra's MCP server is fundamentally different.

What Sentra's MCP Server Actually Does

Sentra's MCP server exposes 130+ tools across 13+ security domains, covering not just queries but write operations, composite investigations, and guided workflows. It's not just a chatbot layer on top of a dashboard. It's a full security operations interface.

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The difference is the gap between observing and operating. Sentra's MCP server closes the loop from detection to response.

Real Workflow: One Prompt, One Complete Policy Audit

Here's a real prompt a security engineer used during a policy noise reduction exercise:

"Audit all enabled security policies. For each policy, show me how many open alerts it generates and its severity. Identify policies that generate more than 50 low-severity alerts, those are candidates for tuning. For the noisiest policy, show me a sample violated assets so I can determine if it's misconfigured. Then disable that policy and resolve its existing alerts."

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Behind the scenes, the MCP server chains 6+ tools to fulfill this request:

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1. `policies_get_all` -- Retrieves all enabled policies with severity metadata
2. `policies_get_policy_incidents_count` -- Gets open alert counts per policy
3. `alerts_get_all_external` -- Fetches alerts filtered to the noisiest policy
4. `alerts_get_violated_store_data_assets_by_alert` -- Shows sample violated assets for review
5. `policy_change_status` -- Disables the misconfigured policy (write operation)
6. `alert_transition` -- Resolves existing alerts with reason "false_positive" (write operation)

No script. No runbook. No context switching between tabs. A single natural language prompt drove an end-to-end audit-to-remediation workflow that would typically take an engineer 30-60 minutes of manual work.

This is what "from observing to operating" looks like in practice.

6 Ready-to-Use Prompts for Data Security Posture Management

The policy audit above is just one example. Sentra's MCP server supports a progression from simple queries to complex, multi-tool operations:

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Quick status check: "Show me open alerts by severity and our current security rating." Two tools fire, you get a snapshot in seconds.

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Compliance audit preparation: "Prepare HIPAA compliance evidence: show all controls, our compliance score, open violations, and data classification coverage for PHI." The compliance_audit_prep workflow prompt chains 6+ tools into an audit-ready report.

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Alert triage and resolution: "Investigate alert abc-123: what data is at risk, who has access, is this recurring? If it's a false positive, resolve it with a comment explaining why." The investigate_alert composite tool gathers details, blast radius, and history in one call. Then write operations close the loop.

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Identity access review: "Show me all external identities with access to high-sensitivity stores. For the riskiest one, map the full access graph from identity to roles to stores to assets." Identity search, graph traversal, and sensitivity analysis,all through conversation.

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Board-ready security briefing: "Prepare my quarterly board briefing: posture trends for 90 days, compliance status by framework, open alerts by severity, security rating trend, and top 5 recommendations." The security_posture_summary composite tool pulls dashboard, alerts, ratings, compliance, risk distribution, and sensitivity data in one call.

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AI data risk assessment: "Show me all AI-related assets, what sensitive data they contain, who has access to training data, and whether there are security alerts on those stores." Dedicated AI/ML asset tools surface machine learning risks that traditional DSPM tools miss.

Enterprise-Grade Architecture

Conversational doesn't mean casual. Sentra's MCP server is built for production security operations:

• Connection pooling via a shared httpx.AsyncClient with keep-alive for sustained performance
• Automatic retry with exponential backoff for rate limits (429) and server errors (5xx)
• SSRF protection that blocks requests to private/metadata IP ranges
• 6-tier write operation hierarchy -- from additive-only comments (Tier 1) up to destructive operations requiring explicit safety confirmation (Tier 6)
• Feature flag control -- all write operations gated by SENTRA_ENABLE_WRITE_OPS, disabled with a single environment variable
• UUID validation on all identifier parameters before HTTP calls are made
• Error sanitization that strips internal details (hostnames, file paths) from client-facing responses
• TLS-native deployment with certificate configuration for direct HTTPS serving
• API key authentication on the MCP endpoint itself, separate from Sentra API credentials

Getting Started

Three deployment paths, from local development to production:

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Claude Desktop (local, stdio): Add Sentra's MCP server to your Claude Desktop configuration. Point it at your Sentra API key, and start asking questions. Zero infrastructure required.

Claude Code / Cursor (developer workflow): Run the MCP server alongside your IDE. Security engineers get conversational access to Sentra while they work, without switching contexts.

Docker (production, HTTP transport): Deploy as a containerized service with TLS, API key authentication, and CORS controls. Multiple AI agents or team members can connect to a single shared instance.

All three paths expose the same 130+ tools, 11 write operations, 5 guided workflows, and 2 composite investigation tools.

The Future of Data Security Operations Is Conversational

The security industry spent the last decade building visibility. We can see everything. The challenge now is turning that visibility into action at the speed modern environments demand. Sentra's MCP server represents a fundamental shift: from dashboards you read to agents that operate. From runbooks that describe steps to AI that executes them. From alert fatigue to conversational triage and resolution.

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The tools are real. The write operations are real. The workflows are real. And they're available today.

Investigate, triage, and resolve - not just query. That's the difference between an MCP server that observes and one that operates.

Sentra's MCP server is available now for existing customers. Schedule a Demo to see how it works.

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Walk into any aerospace, defense, semiconductor or industrial design organization and you’ll find one file format everywhere: AutoCAD’s DWG. These drawings are the blueprints for missiles, fabs, turbines, containment domes and critical infrastructure. They’re also one of the biggest blind spots in most data security programs. Traditional DSPM and DLP tools see a DWG as a big opaque blob: “binary, probably sensitive, treat with caution.” That’s no longer good enough if you are operating under ITAR, EAR or handling multi‑billion‑dollar IP assets.

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This is why we built native DWG parsing into Sentra. We now read AutoCAD DWG files directly, with no AutoCAD license, no intermediate conversion and no third‑party libraries. For the first time, security and compliance teams can discover, classify and monitor the sensitive data hiding inside CAD drawings across cloud storage, file shares and engineering data lakes.

Why DWG Has Been Invisible to Security

As a CTO I’ve sat in many reviews where teams are confident they know where PII lives and where source code lives. When I ask, “What about your CAD drawings?” the room usually goes quiet.

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DWG is a proprietary binary format, engineered for performance and fidelity, not for generic content inspection. Security tools that rely on text extraction or simple file signatures can’t see anything meaningful inside it. On top of that, CAD is often considered “engineering’s problem.” Drawings live on legacy engineering servers, PLM systems, or “temporary” project shares that never get decommissioned. When those repositories are lifted and shifted to S3, Azure Blob or SharePoint, security inherits terabytes of DWG files with almost no insight into what they actually contain.

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Regulations add more pressure. ITAR and EAR talk about “technical data,” but the tooling most teams use for export‑control compliance was built around PDFs and Office documents, not native CAD formats. The result is predictable: either every DWG is treated as maximally toxic—which paralyzes engineering—or they’re collectively ignored, which is worse.

We wanted to break that stalemate by making DWG as transparent to security teams as a Word document.

What’s Really Inside a DWG File?

A DWG file is far more than geometry. It’s a container for rich metadata, text and structural elements that describe both the design and its context.

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Sentra’s parser now extracts several key categories of information:

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• Document properties such as author, “last saved by,” creation and modification timestamps, total editing time and revision counters. This tells you who touched a drawing and when.
• Title block attributes where engineering teams encode drawing numbers, project IDs, revision codes, department names, approvers and—crucially—export control markings like ECCN codes and ITAR statements.
• Text content from notes, MText blocks, labels and callouts. This is where you see manufacturing tolerances, material specifications, part numbers and phrases like “COMPANY CONFIDENTIAL” or “EXPORT CONTROLLED.”
• Layer names, which engineers often use to signal sensitivity or ownership:
  ITAR-CONTROLLED, PROPRIETARY, CLIENT-CONFIDENTIAL, CLASSIFIED-GEOMETRY, and so on.
• Application metadata such as the AutoCAD version, build and locale that created the file. That can help tie drawings back to specific offices or workstation groups.
• File dependencies and paths including fonts, external references (xrefs), plot configurations and linked drawings. These paths routinely expose server names, share names, usernames and department structures.

If you’re an attacker, that metadata is a reconnaissance goldmine. If you’re running security for a regulated engineering environment, it’s exactly the context you’ve been missing.

Why DWG Data Is Exceptionally Sensitive

Literal blueprints of your IP

In many organizations, DWGs are the most literal representation of intellectual property that exists. They encode the shape of a missile fin, the trace layout of a secure ASIC, or the reinforcement pattern of a containment vessel. A leaked drawing isn’t a description of the product—it is the product. Unlike a slide deck or a spec sheet, a DWG often contains everything a capable adversary needs to replicate or attack the system. That makes these files high‑value targets for nation‑state actors and sophisticated competitors.

Export control and regulatory risk

For companies operating under ITAR and EAR, DWGs are typically where export‑controlled “technical data” actually lives.

The ECCN code or ITAR statement is rarely in the filename or the folder name. It’s embedded in the title block attributes and in annotations on the page. A single file with those markings sitting in an uncontrolled S3 bucket, or shared via a public link, can trigger a regulatory violation with multi‑million‑dollar consequences and long‑term impact on your ability to win future contracts.

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Because Sentra parses DWGs directly, we can programmatically answer questions like:

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• “Show me every DWG in our cloud environment that contains an ITAR statement or ECCN code.”
• “Where exactly are those files stored, and who can access them?”

That’s impossible to do reliably if you treat DWGs as opaque binary blobs.

Supply‑chain exposure

Drawings don’t stay within a single company. They flow between primes, subcontractors, design houses, manufacturers and integration partners. Each stop along that chain leaves traces: author names, revision histories, local file paths, department identifiers. When you ingest a partner’s DWG, you’re often ingesting their sensitive operational metadata as well as your own IP. That creates both an obligation to protect it and an opportunity for attackers to learn about everyone involved in your programs.

People and infrastructure reconnaissance

From an attacker’s perspective, seemingly benign fields like “Last saved by,” or dependency paths like \\ENGSERVER03\Projects\F35-Wing\Stress\ are a treasure map. They reveal usernames, project names, server names and network topology.

From a defender’s perspective, that same metadata is invaluable for incident response and insider‑risk investigations—if you can see it.

How Security Teams Are Already Using DWG Parsing

Let me make this more concrete with a few patterns we’re seeing in early deployments.

Discovering export‑controlled drawings in cloud storage

An aerospace manufacturer had migrated years of engineering history from on‑premises file servers into S3 and Azure Blob. They knew “there’s a lot of CAD in there,” but they couldn’t distinguish a generic fixture drawing from a file that actually carried ITAR or EAR restrictions.

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With Sentra scanning those buckets, they can now automatically identify DWGs whose title blocks or annotations contain ITAR statements, ECCN codes or proprietary markings. That means they can focus remediation and access reviews on the subset of drawings that are actually regulated, instead of blanket‑treating every DWG the same way.

Engineers get fewer unnecessary reviews. Security gets a precise map of where controlled technical data lives in cloud storage.

Monitoring technical data exfiltration via collaboration platforms

Another customer, an energy company, shares drawings with EPC contractors through SharePoint, OneDrive and Box. Hundreds of DWGs move every week. Previously, they had no idea whether the files shared externally described generic mounting brackets or detailed layouts of protected infrastructure.

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By parsing DWGs inline as they pass through those platforms, Sentra can now flag drawings whose contents match sensitive keywords, export‑control markings, or proprietary statements. Security teams see alerts like “DWG with ITAR language shared with external account” rather than “some DWG went out,” which is what most tools can tell you today.

Building a defensible ITAR audit trail

A defense contractor we work with has to periodically prove to auditors that all ITAR‑controlled technical data is stored and processed only in approved regions and systems. Historically they relied on manual attestations from engineering teams and small sample reviews.

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Now they scan every DWG in scope with Sentra. We generate an inventory of all drawings that contain ITAR or EAR markings, map each file to its exact storage location and access control set, and surface any out‑of‑policy placements. When an auditor asks “Show us where your ITAR technical data is,” they can answer with data, not with a slide deck.

How Our DWG Parser Works

From an engineering standpoint, we wanted a solution that was:

• Native: no dependence on AutoCAD or closed‑source SDKs.
• Wide‑ranging: support for virtually all real‑world DWG files.
• Predictable: deterministic behavior at petabyte scale.

We implemented a parser that reads the binary DWG format directly, supporting AutoCAD versions from 2000 through 2024 (formats AC1015 through AC1032). There’s no AutoCAD installation required anywhere in the environment. We don’t convert files to DXF, PDF or images. We don’t send data to external services.

All parsing happens where Sentra runs—inside the customer’s cloud accounts or VPCs—so sensitive technical data never leaves their control.

Closing the Gap Between “Stored” and “Understood”

DWG support is part of a broader direction for Sentra. As more specialized workloads move to the cloud—EDA, PLM, simulation, scientific computing -the number of proprietary and domain‑specific file formats in your environment explodes.

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Most security tools weren’t built for that world. They know how to read emails and office documents. They can fingerprint code repositories. But they look at a DWG, a GDSII, or a proprietary simulation output and shrug.

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The reality is simple:

You cannot secure data you don’t understand.

Understanding means being able to answer, at scale, not only “Where is this file?” but “What is inside this file, and how sensitive is it?”

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For organizations in aerospace, defense, energy, manufacturing and other technical industries, DWG files are often where your most tightly regulated and most commercially valuable data lives. Being able to automatically discover and classify that content is not a nice‑to‑have. It’s a compliance requirement that has been hiding in plain sight.

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If you want to see what’s actually hiding in your own drawings, the easiest next step is to run a focused assessment: pick a few representative buckets or repositories, let Sentra scan the DWGs in place, and look at the inventory of export‑controlled and proprietary designs that surfaces.

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My experience is that once you see those results, you’ll never look at “just another CAD file” the same way again.

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SOC 2 has become table stakes for cloud‑native and SaaS organizations. But for many security and GRC teams, each SOC 2 cycle still feels like starting from scratch; hunting for the latest access reviews, exporting encryption settings from multiple consoles, proving backups and logs exist - per data set, per environment. If your SOC 2 evidence process is still a patchwork of spreadsheets and screenshots, you’re not alone. The missing piece is a data‑centric view of your controls, especially around regulated data.

Why SOC 2 Evidence Is So Hard in Cloud and SaaS Environments

Under SOC 2, trust service criteria like Security, Availability, and Confidentiality translate into specific expectations around data:

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Is sensitive or regulated data discovered and classified consistently?

Are core controls (encryption, backup, access, logging) actually in place where that data lives?

Can you show continuous monitoring instead of point‑in‑time screenshots?

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In a typical multi‑cloud/SaaS environment:

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• Sensitive data is scattered across S3, databases, Snowflake, M365/Google Workspace, Salesforce, and more.
• Different teams own pieces of the puzzle (infra, security, data, app owners).
• Legacy tools are siloed by layer (CSPM for infra, DLP for traffic, privacy catalog for RoPA).

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So when SOC 2 comes around, you spend weeks assembling a story instead of being able to show a trusted, provable compliance posture at the data layer.

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The Data‑First Approach to SOC 2 Evidence

Instead of treating SOC 2 as a separate project, leading teams are aligning it with their data security posture management (DSPM) strategy:

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1. Start from the data, not from the infrastructure

• Build a unified inventory of sensitive and regulated data across IaaS, PaaS, SaaS, and on‑prem.
• Enrich each store with sensitivity, residency, and business context.

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2. Attach control posture to each data store

• For each regulated data store, track encryption status, backup configuration, access model, and logging/monitoring coverage as posture attributes.

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3. Generate SOC‑aligned evidence from the same system

• Use the regulated‑data inventory plus posture engine to produce SOC 2‑friendly reports and CSVs, rather than collecting evidence manually for each audit cycle.

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This is exactly the pattern that modern data security platforms like Sentra are implementing.

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How Sentra Helps Security and GRC Teams Automate SOC 2 Evidence

Sentra sits across your data estate and focuses on regulated data, with capabilities that map directly onto SOC 2 evidence needs:

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Comprehensive data‑store discovery and classification
Agentless discovery of data stores (managed and unmanaged) across multi‑cloud and on‑prem, combined with high‑accuracy classification for regulated and business‑critical data.

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Data‑centric security posture
For each store, Sentra tracks security properties—including encryption, backup, logging, and access configuration, and surfaces gaps where sensitive data is insufficiently protected.

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Framework‑aligned reporting
SOC 2 and other frameworks can be represented as report templates that pull directly from Sentra’s inventory and posture attributes, giving GRC teams “audit‑ready” exports without rebuilding evidence from scratch.

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The result is you can prove control over regulated data, for SOC 2 and beyond, with far less manual overhead.

Mapping SOC 2 Criteria to Data‑Level Evidence

Here’s how a data‑first posture shows up in SOC 2:

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CC6.x (Logical and Physical Access Controls)

Evidence: Identity‑to‑data mapping showing which users/roles can access which sensitive datasets across cloud and SaaS.

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CC7.x (Change Management / Monitoring)

Evidence: Data Detection & Response (DDR) signals and anomaly analytics around access to crown‑jewel data; logs that tie back to sensitive data stores.

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CC8.x (Risk Mitigation)

Evidence: Risk‑prioritized view of data stores based on sensitivity and missing controls, plus remediation workflows or automatic labeling/tagging to tighten upstream policies.

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When this data‑level view is in place, SOC 2 becomes evidence selection rather than evidence construction.

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A Repeatable SOC 2 Playbook for Security, GRC, and Privacy

To operationalize this approach, many teams follow a recurring pattern:

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1. Define a “regulated data perimeter” for SOC 2: Identify which clouds, SaaS platforms, and on‑prem stores contain in‑scope data (PII, PHI, PCI, financial records).

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2. Instrument with DSPM: Deploy a data security platform like Sentra to discover, classify, and map access to that data perimeter.

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3. Connect GRC to the same source of truth: Have GRC and privacy teams pull their SOC 2 evidence from the same inventory and posture views Security uses for day‑to‑day risk management.

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4. Continuously refine controls: Use posture and DDR insights to reduce exposure, close misconfigurations, and improve your next SOC 2 cycle before it starts.

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The more you lean on a shared, data‑centric foundation, the easier it becomes to maintain a trusted, provable compliance posture across frameworks, not just SOC 2.

Turning SOC 2 From a Project Into a Capability

Ultimately, the goal is to stop treating SOC 2 as a once-a-year project and start treating it as an ongoing capability embedded into how your organization operates. Security, GRC, and privacy teams should work from a single, unified view of regulated data and controls. Evidence should always be a few clicks away - not the result of a month-long scramble. And every audit should strengthen your data security posture, not distract from it. If you’re still managing compliance in spreadsheets, it’s worth asking what it would take to make your SOC 2 posture something you can prove on demand.

Ready to end the fire drills and move to continuous compliance? Book a Demo 

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As cloud environments grow more complex in 2026, knowing how to discover sensitive data in the cloud has become one of the most pressing challenges for security and compliance teams. Data sprawls across IaaS, PaaS, SaaS platforms, and on-premise file shares, often duplicating, moving between environments, and landing in places no one intended. Without a systematic approach to discovery, organizations risk regulatory exposure, unauthorized AI access, and costly breaches. This article breaks down the key methods, tools, and architectural considerations that make cloud sensitive data discovery both effective and scalable.

Why Sensitive Data Discovery in the Cloud Is So Difficult

The core problem is visibility. Sensitive data, PII, financial records, health information, intellectual property, doesn't stay in one place. It gets copied from production to development environments, ingested into AI pipelines, backed up across regions, and shared through SaaS applications. Each transition creates a new exposure surface.

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• Toxic combinations: High-sensitivity data behind overly permissive access configurations creates dangerous scenarios that require continuous, context-aware monitoring, not just point-in-time scans.
• Shadow and ROT data: Redundant, obsolete, or trivial data inflates cloud storage costs and expands the attack surface without adding business value.
• Multi-environment sprawl: Data moves across cloud providers, regions, and service tiers, making a single unified view extremely difficult to maintain.

What Are Cloud DLP Solutions and How Do They Work?

Cloud Data Loss Prevention (DLP) solutions discover, classify, and protect sensitive information across cloud storage, applications, and databases. They operate through several interconnected mechanisms:

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• Scan and classify: Pattern matching, machine learning, and custom detectors identify sensitive content and assign classification labels (e.g., public, confidential, restricted).
• Enforce automated policies: Context-aware rules trigger encryption, masking, or access restrictions based on classification results.
• Monitor data movement: Continuous tracking of transfers and user behaviors detects anomalies like unusual download patterns or overly broad sharing.
• Integrate with broader controls: Many DLP tools work alongside CASBs and Zero Trust frameworks for end-to-end protection.

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The result is enhanced visibility into where sensitive data lives and a proactive enforcement layer that reduces breach risk while supporting regulatory compliance.

What Is Google Cloud Sensitive Data Protection?

Google Cloud Sensitive Data Protection is a cloud-native service that automatically discovers, classifies, and protects sensitive information across Cloud Storage buckets, BigQuery tables, and other Google Cloud data assets.

Core Capabilities

• Automated discovery and profiling: Scans projects, folders, or entire organizations to generate data profiles summarizing sensitivity levels and risk indicators, enabling continuous monitoring at scale.
• Detailed data inspection: Performs granular analysis using hundreds of built-in detectors alongside custom infoTypes defined through dictionaries, regular expressions, or contextual rules.
• De-identification techniques: Supports redaction, masking, and tokenization, making it a strong foundation for data governance within the Google Cloud ecosystem.

How Sensitive Data Protection’s Data Profiler Finds Sensitive Information

Sensitive Data Protection’s data profiler automates scanning across BigQuery, Cloud SQL, Cloud Storage, Vertex AI datasets, and even external sources like Amazon S3 or Azure Blob Storage (for eligible Security Command Center customers). The process starts with a scan configuration defining scope and an inspection template specifying which sensitive data types to detect.

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These profiles give security and governance teams an always-current view of where sensitive data resides and how risky each asset is.

Understanding Sensitive Data Protection Pricing

Sensitive Data Protection primarily uses per-GB profiling charges, billed based on the amount of input data scanned, with minimums and caps per dataset or table. Certain tiers of Security Command Center include organization-level discovery as part of the subscription, but for most workloads several factors directly influence total cost:

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For organizations operating at petabyte scale, these factors make it essential to design discovery workflows carefully rather than running broad, undifferentiated scans.

Tracking Data Movement Beyond Static Location

Static discovery, knowing where sensitive data sits right now, is necessary but insufficient. The real risk often emerges when data moves: from production to development, across regions, into AI training pipelines, or through ETL processes.

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• Data lineage tracking: Captures transitions in real time, not just periodic snapshots.
• Boundary crossing detection: Flags when sensitive assets cross environment boundaries or land in unexpected locations.
• Practical example: Detecting when PII flows from a production database into a dev environment is a critical control, and requires active movement monitoring.

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This is where platforms differ significantly. Some tools focus on cataloging data at rest, while more advanced solutions continuously monitor flows and surface risks as they emerge.

How Sentra Approaches Sensitive Data Discovery at Scale

Sentra is built specifically for the challenges described throughout this article. Its agentless architecture connects directly to cloud provider APIs without inline components on your data path and operates entirely in-environment, so sensitive data never leaves your control for processing. This design is critical for organizations with strict data residency requirements or preparing for regulatory audits.

Key Capabilities

• Unified multi-environment coverage: Spans IaaS, PaaS, SaaS, and on-premise file shares with AI-powered classification that distinguishes real sensitive data from mock or test data.
• DataTreks™ mapping: Creates an interactive map of the entire data estate, tracking active data movement including ETL processes, migrations, backups, and AI pipeline flows.
• Toxic combination detection: Surfaces sensitive data behind overly broad access controls with remediation guidance.
• Microsoft Purview integration: Supports automated sensitivity labeling across environments, feeding high-accuracy labels into Purview DLP and broader Microsoft 365 controls.

What Users Say (Early 2026)

Strengths:

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• Classification accuracy: Reviewers note it is “fast and most accurate” compared to alternatives.
• Shadow data discovery: “Brought visibility to unstructured data like chat messages, images, and call transcripts” that other tools missed.
• Compliance facilitation: Teams report audit preparation has become significantly more manageable.

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Considerations:

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• Initial learning curve with the dashboard configuration.
• On-premises capabilities are less mature than cloud coverage, relevant for organizations with significant legacy infrastructure.

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Beyond security, Sentra's elimination of shadow and ROT data typically reduces cloud storage costs by approximately 20%, extending the business case well beyond compliance.

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For teams looking to understand how to discover sensitive data in the cloud at enterprise scale, Sentra's Data Discovery and Classification offers a comprehensive starting point, and its in-environment architecture ensures the discovery process itself doesn't introduce new risk.

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AI assistants, SaaS, and hybrid work have made data easier than ever to discover, share, and reuse. Tools like Gemini for Google Workspace and Microsoft 365 Copilot can search across drives, mailboxes, chats, and documents in seconds - surfacing information that used to be buried in obscure folders and old snapshots.

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That’s great for productivity, but dangerous for data security.

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Traditional, policy‑based DLP wasn’t designed to handle this level of complexity. At the same time, many organizations now use DSPM tools to understand where their sensitive data lives, but still lack real‑time control over how that data moves on endpoints, in browsers, and across SaaS.

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Together, Sentra and Orion close this gap: Sentra brings next‑gen, context-driven DSPM; Orion brings next‑gen, behavior‑driven DLP. The result is end‑to‑end, AI‑ready data protection from data store to last‑mile usage, creating a learning, self‑improving posture rather than a static set of controls.

Why DSPM or DLP Alone Isn’t Enough

Modern data environments require two distinct capabilities: deep data intelligence and real-time enforcement based on contextual business context.

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DSPM solutions provide a data-centric view of risk. They continuously discover and classify sensitive data across cloud, SaaS, and on-prem environments. They map exposure, detect shadow data, and surface over-permissioned access. This gives security teams a clear understanding of what sensitive data exists, where it resides, who can access it, and how exposed it is.

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DLP solutions operate where data moves - on endpoints, in browsers, across SaaS, and in email. They enforce policies and prevent exfiltration as it happens. 

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Without rich data context like accurate sensitivity classification, exposure mapping, and identity-to-data relationships, DLP solutions often rely on predefined rules or limited signals to decide what to block, allow, or escalate.

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DLP can be enforced, but its precision depends on the quality of the data intelligence behind it.

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In AI-enabled, multi-cloud environments, visibility without enforcement is insufficient - and enforcement without deep data understanding lacks precision. To protect sensitive data from discovery by AI assistants, misuse across SaaS, or exfiltration from endpoints, organizations need accurate, continuously updated data intelligence, real-time, context-aware enforcement, and feedback between the two layers. 

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That is where Sentra and Orion complement each other.

Sentra: Data‑Centric Intelligence for AI and SaaS

Sentra provides the data foundation: a continuous, accurate understanding of what you’re protecting and how exposed it is.

Deep Discovery and Classification

Sentra continuously discovers and classifies sensitive data across cloud‑native platforms, SaaS, and on‑prem data stores, including Google Workspace, Microsoft 365, databases, and object storage. Under the hood, Sentra uses AI/ML, OCR, and transcription to analyze both structured and unstructured data, and leverages rich data class libraries to identify PII, PHI, PCI, IP, credentials, HR data, legal content, and more, with configurable sensitivity levels.

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This creates a live, contextual map of sensitive data: what it is, where it resides, and how important it is.

Reducing Shadow Data and Exposure

Sentra helps teams clean up the environment before AI and users can misuse it. 

It uncovers shadow data and obsolete assets that still carry sensitive content, highlights redundant or orphaned data that increases exposure (without adding business value), and supports collaborative workflows for remediation for security, data, and app owners.

Access Governance and Labeling for AI and DLP

Sentra turns visibility into governance signals. It maps which identities have access to which sensitive data classes and data stores, exposing overpermissioning and risky external access, and driving least‑privilege by aligning access rights with sensitivity and business needs.

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To achieve this, Sentra automatically applies and enforces:

Google Labels across Google Drive, powering Gemini controls and DLP for Drive, and Microsoft Purview Information Protection (MPIP) labels across Microsoft 365, powering Copilot and DLP policies.

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These labels become the policy fabric downstream AI and DLP engines use to decide what can be searched, summarized, or shared.

Orion: Behavior‑Driven DLP That Thinks Beyond Policies

Orion replaces policy reliance with a set of intelligent, context-aware proprietary AI agents

AI Agents That Understand Context

Orion’s agents collect rich context about data, identity, environment, and business relationships. 

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This includes mapping data lineage and movement patterns from source to destination, a contextual understanding of identities (role, department, tenure, and more), environmental context (geography, network zone, working hours), external business relationships (vendor/customer status), Sentra’s data classification, and more. 

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Based on this rich, business-aware context, Orion’s agents detect indicators of data loss and stop potential exfiltrations before they become incidents. That means a full alignment between DLP and how your business actually operates, rather than how it was imagined in static policies.

Unified Coverage Where Data Moves

Orion is designed as a unified DLP solution, covering: 

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• Endpoints
• SaaS applications
• Web and cloud
• Email
• On‑prem and storage, including channels like print

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From initial deployment, Orion quickly provides meaningful detections grounded in real behavior, not just pattern hits. Security teams then get trusted, high‑quality alerts.

Better Together: End‑to‑End, AI‑Ready Protection

Individually, Sentra and Orion address critical yet distinct challenges. Together, they create a closed loop:

Sentra → Orion: Smarter Detections

Sentra gives Orion high‑quality context:

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• Which assets are truly sensitive, and at what level.
• Where they live, how widely they’re exposed, and which identities can reach them.
• Which documents and stores carry labels or policies that demand stricter treatment.

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Orion uses this information to prioritize and enrich detections, focusing on events involving genuinely high‑risk data. It can then adapt behavior models to each user and data class, improving precision over time.

Orion → Sentra: Real‑World Feedback

Orion’s view into actual data movement feeds back into Sentra, exposing data stores that repeatedly appear in risky behaviors and serve as prime candidates for cleanup or stricter access governance. It also highlights identities whose actions don’t align with their expected access profile, feeding Sentra’s least‑privilege workflows. This turns data protection into a self‑improving system instead of a set of static controls.

What this means for Security and Risk Teams

With Sentra and Orion together, organizations can:

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• Securely adopt AI assistants like Gemini and Copilot, with Sentra controlling what they can see and Orion controlling how data is actually used on endpoints and SaaS.
• Eliminate shadow data as an exfil path by first mapping and reducing it with Sentra, then guarding remaining high‑risk assets with Orion until they’re remediated.
• Make least‑privilege real, with Sentra defining who should have access to what and Orion enforcing that principle in everyday behavior.
• Provide auditors and boards with evidence that sensitive data is discovered, governed, and protected from exfiltration across both data platforms and endpoints.

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Instead of choosing between “see everything but act slowly” (DSPM‑only) and “act without deep context” (DLP‑only), Sentra and Orion let you do both well - with one data‑centric brain and one behavior‑aware nervous system.

Ready to See Sentra + Orion in Action?

If you’re looking to secure AI adoption, reduce data loss risk, and retire legacy DLP noise, the combination of Sentra DSPM and Orion DLP offers a practical, modern path forward.

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See how a unified, AI‑ready data protection architecture can look in your environment by mapping your most critical data and exposures with Sentra, and letting Orion protect that data as it moves across endpoints, SaaS, and web in real time.

Request a joint demo to explore how Sentra and Orion together can help you think beyond policies and build a data protection program designed for the AI era.

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Enterprises managing sprawling multi-cloud environments struggle to keep ROPA (Records of Processing Activities) reporting accurate and up to date for GDPR compliance. As manual, spreadsheet-based workflows hit their limits, automation has become essential - not just to save time, but to build confidence in what data is actually being processed across the organization.

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Recently, during a strategy session, a leading GDPR-regulated customer shared how they are using Sentra to move beyond manual ROPA processes. By relying on Sentra’s automated data discovery, AI-driven classification, and environment-aware reporting, the organization has operationalized a high-confidence ROPA across ~100 cloud accounts. Their experience highlights a critical shift: ROPA as a trusted source of truth rather than a checkbox exercise.

Why ROPA Often Comes Up Short in Practice

For many organizations, maintaining a ROPA is a regulatory requirement, but not a reliable one.

As the customer explained:

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“What I’ve often seen is the ROPA or the records of processing activity being something that is a very checkbox thing to do. And that’s because it’s really hard to understand what data you actually have unless you literally go and interrogate every database.”

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Without direct visibility into cloud data stores, ROPA documentation often relies on assumptions, interviews, and outdated spreadsheets. This approach doesn’t scale and creates risk during audits, due diligence, and regulatory inquiries, especially for companies operating across multiple clouds or growing through acquisition.

From Guesswork to a High-Confidence ROPA

The same customer described how Sentra fundamentally changed their approach:

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“What Sentra allowed us to do is really have what I’ll describe as a high confidence ROPA. Our ROPA wasn’t guesswork, it was based on actual information that Sentra had gone out, touched our databases, looked inside them, identified the specific types of data records, and then gave us that inventory of what we had.”

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By directly scanning databases and cloud data stores, Sentra replaces assumptions with facts. ROPA reports are generated from live discovery results, giving compliance teams confidence that they can accurately attest to:

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• What personal data they hold
• Where it resides
• How it is processed
• And how it is governed

This transforms ROPA from a static document into a defensible, audit-ready asset.

The Need for Automated ROPA Reporting at Scale

Manual ROPA reporting becomes unmanageable as cloud environments expand. Organizations with dozens or hundreds of cloud accounts quickly face gaps, inconsistencies, and outdated records. Industry research shows that privacy automation can reduce manual ROPA effort by up to 80% and overall compliance workload by 60%. But effective automation requires focus. Reporting must concentrate on production environments, where real customer data lives, rather than drowning teams in noise from test or development systems.

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As a privacy champion on this project, explains:

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“What I’m interested in is building a data inventory that gives me insight from a privacy point of view on what kind of customer data we are holding.”

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This shift toward privacy-focused inventories ensures ROPA reporting stays meaningful, actionable, and aligned with regulatory intent.

How Sentra Enables Template-Driven, Environment-Aware ROPA Reporting

Sentra’s reporting framework allows organizations to create custom ROPA templates tailored to their regulatory, operational, and business needs. These templates automatically pull from continuously updated discovery and classification results, ensuring reports stay accurate as environments evolve.

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A critical component of this approach is environment tagging. By clearly distinguishing production systems from non-production environments, Sentra ensures ROPA reports reflect only systems that actually process personal data. This reduces reporting noise, improves audit clarity, and aligns with modern GDPR automation best practices.

The result is ROPA reporting that is both scalable and precise - without requiring manual filtering or spreadsheet maintenance.

Solving the Data Classification Problem with Context-Aware AI

Accurate ROPA automation depends on intelligent data classification. Many tools rely on basic pattern matching, which often leads to false positives, such as mistaking airline or airport codes for regulated personal data in HR or internal systems.

Sentra addresses this challenge with AI-based, context-aware classification that understands how data is structured, where it appears, and how it is used. Rather than flagging data solely based on patterns, Sentra analyzes context to reliably distinguish between regulated personal data and non-regulated business data.

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This approach dramatically reduces false positives and gives privacy teams confidence that ROPA reports reflect real regulatory exposure - without manual cleanup, lookup tables, or ongoing tuning.

What Sets Sentra Apart for ROPA Automation

While many platforms claim to support ROPA automation, few can deliver accurate, production-ready reporting across complex cloud environments. Sentra stands out through:

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• Agentless data discovery
• Native multi-cloud support (AWS, Azure, GCP, and hybrid)
• Context-aware AI classification
• Data-centric inventory of all customer regulated data
• Flexible, customizable ROPA reporting templates
• Strong handling of inconsistent metadata and environment tagging
  
  

As the customer summarized:

“It’s no longer a checkbox exercise. It’s a very high confidence attestation of what we definitely have. That visibility allowed us to comply with GDPR in a much more comprehensive way.”

Conclusion

ROPA automation is not just about efficiency, it’s about trust. By grounding ROPA reporting in real data discovery, environment awareness, and AI-driven classification, Sentra enables organizations to replace guesswork with confidence.

The result is a scalable, defensible ROPA that reduces manual effort, lowers compliance risk, and supports long-term privacy maturity.

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Interested in seeing high-confidence ROPA automation in action? Book a demo with Sentra to learn how you can turn ROPA into a living source of truth for GDPR compliance.

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Most security teams think of data classification as a single capability. A tool scans data, finds sensitive information, and labels it. Problem solved. In reality, modern data environments have made classification far more complex.

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As organizations scale across cloud platforms, SaaS apps, data lakes, collaboration tools, and AI systems, security teams must answer two fundamentally different questions:

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1. What sensitive data exists inside this asset?
2. What is this asset actually about?

These questions represent two distinct approaches:

• Entity-level data classification
• File-level (asset-level) data classification

A well-functioning Data Security Posture Management (DSPM) requires both.

What Is Entity-Level Data Classification?

Entity-level classification identifies specific sensitive data elements within structured and unstructured content. Instead of labeling an entire file as sensitive, it determines exactly which regulated entities are present and where they appear. These entities can include personal identifiers, financial account numbers, healthcare codes, credentials, digital identifiers, and other protected data types.

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This approach provides precision at the field or token level. By detecting and validating individual data elements, security teams gain measurable visibility into exposure - including how many sensitive values exist, where they are located, and how they are used. That visibility enables targeted controls such as masking, redaction, tokenization, and DLP enforcement. In cloud and AI-driven environments, where risk is often tied to specific identifiers rather than document categories, this level of granularity is essential.

Examples of Entity-Level Detection

Entity-level classifiers detect atomic data elements such as:

• Personal identifiers (names, emails, Social Security numbers)
• Financial data (credit card numbers, IBANs, bank accounts)
• Healthcare markers (diagnoses, ICD codes, treatment terms)
• Credentials (API keys, tokens, private keys, passwords)
• Digital identifiers (IP addresses, device IDs, user IDs)

This level of granularity enables precise policy enforcement and measurable risk assessment.

How Entity-Level Classification Works

High-quality entity detection is not just regex scanning. Effective systems combine multiple validation layers to reduce false positives and increase accuracy:

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• Deterministic patterns (regular expressions, format checks)
• Checksum validation (e.g., Luhn algorithm for credit cards)
• Keyword and proximity analysis
• Dictionaries and structured reference tables
• Natural Language Processing (NLP) with Named Entity Recognition
• Machine learning models to suppress noise

This multi-signal approach ensures detection works reliably across messy, real-world data.

When Entity-Level Classification Is Essential

Entity-level classification is essential when security controls depend on the presence of specific data elements rather than broad document categories. Many policies are triggered only when certain identifiers appear together ,such as a Social Security number paired with a name - or when regulated financial or healthcare data exceeds defined thresholds. In these cases, security teams must accurately locate, validate, and quantify sensitive fields to enforce controls effectively.

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This precision is also required for operational actions such as masking, redaction, tokenization, and DLP enforcement, where controls must be applied to exact values instead of entire files. In structured data environments like databases and warehouses, entity-level classification enables column- and table-level visibility, forming the basis for exposure measurement, risk scoring, and access governance decisions.

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However, entity-level detection does not explain the broader business context of the data. A credit card number may appear in an invoice, a support ticket, a legal filing, or a breach report. While the identifier is the same, the surrounding context changes the associated risk and the appropriate response.

This is where file-level classification becomes necessary.

What Is File-Level (Asset-Level) Data Classification?

File-level classification determines the semantic meaning and business context of an entire data asset.

Instead of asking what sensitive values exist, it asks:

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What kind of document or dataset is this? What is its business purpose?

Examples of File-Level Classification

File-level classifiers identify attributes such as:

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• Business domain (HR, Legal, Finance, Healthcare, IT)
  
• Document type (NDA, invoice, payroll record, resume, contract)
  
• Business purpose (compliance evidence, client matter, incident report)

This context is essential for appropriate governance, access control, and AI safety.

How File-Level Classification Works

File-level classification relies on semantic understanding, typically powered by:

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• Small and Large Language Models (SLMs/LLMs)
• Vector embeddings for topic similarity
• Confidence scoring and ensemble validation
• Trainable models for organization-specific document types

This allows systems to classify documents even when sensitive entities are sparse, masked, or absent.

For example, an employment contract may contain limited PII but still require strict access controls because of its business context.

When File-Level Classification Is Essential

File-level classification becomes essential when security decisions depend on business context rather than just the presence of sensitive strings. For example, enforcing domain-based access controls requires knowing whether a document belongs to HR, Legal, or Finance - not just whether it contains an email address or account number. The same applies to implementing least-privilege access models, where entire categories of documents may need tighter controls based on their purpose.

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File-level classification also plays a critical role in retention policies and audit workflows, where governance rules are applied to document types such as contracts, payroll records, or compliance evidence. And as organizations adopt generative AI tools, semantic understanding becomes even more important for implementing AI governance guardrails, ensuring copilots don’t ingest sensitive HR files or privileged legal documents.

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That said, file-level classification alone is not sufficient. While it can determine what a document is about, it does not precisely locate or quantify sensitive data within it. A document labeled “Finance” may or may not contain exposed credentials or an excessive concentration of regulated identifiers, risks that only entity-level detection can accurately measure.

Entity-Level vs. File-Level Classification: Key Differences

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Each approach solves a different security problem. Relying on only one creates blind spots or false positives. Together, they form a powerful combination.

Why Using Only One Approach Creates Security Gaps

Entity-Only Approaches

Tools focused exclusively on entity detection can:

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• Flag isolated sensitive values without context
  
• Generate high alert volumes
  
• Miss business intent
  
• Treat all instances of the same entity as equal risk
  

A payroll file and a legal complaint may both contain Social Security numbers — but they represent different governance needs.

File-Only Approaches

Tools focused only on semantic labeling can:

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• Identify that a document belongs to “Finance” or “HR”
  
• Apply domain-based policies
  
• Enable context-aware access
  
  

But they may miss:

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• Embedded credentials
  
• Excessive concentrations of regulated identifiers
  
• Toxic combinations of data types (e.g., PII + healthcare terms)
  
  

Without entity-level precision, risk scoring becomes guesswork.

How Effective DSPM Combines Both Layers

The real power of modern Data Security Posture Management (DSPM) emerges when entity-level and file-level classification operate together rather than in isolation. Each layer strengthens the other. Context can reinforce entity validation: for example, a dense concentration of financial identifiers helps confirm that a document truly belongs in the Finance domain or represents an invoice. At the same time, entity signals can refine context. If a file is semantically classified as an invoice, the system can apply tighter validation logic to account numbers, totals, and other financial fields, improving accuracy and reducing noise.

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This combination also enables more intelligent policy enforcement. Instead of relying on brittle, one-dimensional rules, security teams can detect high-risk combinations of data. Personal identifiers appearing within a healthcare context may elevate regulatory exposure. Credentials embedded inside operational documents may signal immediate security risk. An unusually high concentration of identifiers in an externally shared HR file may indicate overexposure. These are nuanced risk patterns that neither entity-level nor file-level classification can reliably identify alone.

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When both layers inform policy decisions, organizations can move toward true risk-based governance. Sensitivity is no longer determined solely by what specific data elements exist, nor solely by what category a document falls into, but by the intersection of the two. Risk is derived from both what is inside the data and what the data represents.

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This dual-layer approach reduces false positives, increases analyst trust, and enables more precise controls across cloud and SaaS environments. It also becomes essential for AI governance, where understanding both sensitive content and business context determines whether data is safe to expose to copilots or generative AI systems.

What to Look for in a DSPM Classification Engine

Not all DSPM platforms treat classification equally.

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When evaluating solutions, security leaders should ask:

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• Does the platform classify and validate sensitive entities beyond basic regex?
  
• Can it semantically identify document type and business domain?
  
• Are entity-level and file-level signals tightly integrated?
  
• Can policies reason across both layers simultaneously?
  
• Does risk scoring incorporate both precision and context?
  

The goal is not simply to “classify data,” but to generate actionable, risk-aligned data  intelligence.

The Bottom Line

Modern data estates are too complex for single-layer classification models. Entity-level classification provides precision, identifying exactly what sensitive data exists and where.

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File-level classification provides context - understanding what the data is and why it exists.

Together, they enable accurate risk detection, effective policy enforcement, least-privilege access, and AI-safe governance. In today’s cloud-first and AI-driven environments, data security posture management must go beyond isolated detections or broad labels. It must understand both the contents of data and its meaning - at the same time.

That’s the new standard for data classification.

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Modern cloud environments contain vast amounts of data stored in object storage services such as Amazon S3, Google Cloud Storage, and Azure Blob Storage. In large organizations, a single data store can contain billions (or even tens of billions) of objects. In this reality, traditional approaches that rely on scanning every file to detect sensitive data quickly become impractical.

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Full object-level inspection is expensive, slow, and difficult to sustain over time. It increases cloud costs, extends onboarding timelines, and often fails to keep pace with continuously changing data. As a result, modern data security platforms must adopt more intelligent techniques to build accurate data inventories and sensitivity models without scanning every object.

Why Object-Level Scanning Fails at Scale

Object storage systems expose data as individual objects, but treating each object as an independent unit of analysis does not reflect how data is actually created, stored, or used.

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In large environments, scanning every object introduces several challenges:

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• Cost amplification from repeated content inspection at massive scale
  
• Long time to actionable insights during the first scan
  
• Operational bottlenecks that prevent continuous scanning
  
• Diminishing returns, as many objects contain redundant or structurally identical data
  
  

The goal of data discovery is not exhaustive inspection, but rather accurate understanding of where sensitive data exists and how it is organized.

The Dataset as the Correct Unit of Analysis

Although cloud storage presents data as individual objects, most data is logically organized into datasets. These datasets often follow consistent structural patterns such as:

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• Time-based partitions
  
• Application or service-specific logs
  
• Data lake tables and exports
  
• Periodic reports or snapshots
  
  

For example, the following objects are separate files but collectively represent a single dataset:

logs/2026/01/01/app_events_001.json

logs/2026/01/02/app_events_002.json

logs/2026/01/03/app_events_003.json

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While these objects differ by date, their structure, schema, and sensitivity characteristics are typically consistent. Treating them as a single dataset enables more accurate and scalable analysis.

Analyzing Storage Structure Without Reading Every File

Modern data discovery platforms begin by analyzing storage metadata and object structure, rather than file contents.

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This includes examining:

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• Object paths and prefixes
  
• Naming conventions and partition keys
  
• Repeating directory patterns
  
• Object counts and distribution
  
  

By identifying recurring patterns and natural boundaries in storage layouts, platforms can infer how objects relate to one another and where dataset boundaries exist. This analysis does not require reading object contents and can be performed efficiently at cloud scale.

Configurable by Design

Sampling can be disabled for specific data sources, and the dataset grouping algorithm can be adjusted by the user. This allows teams to tailor the discovery process to their environment and needs.

Automatic Grouping into Dataset-Level Assets

Using structural analysis, objects are automatically grouped into dataset-level assets. Clustering algorithms identify related objects based on path similarity, partitioning schemes, and organizational patterns. This process requires no manual configuration and adapts as new objects are added. Once grouped, these datasets become the primary unit for further analysis, replacing object-by-object inspection with a more meaningful abstraction.

Representative Sampling for Sensitivity Inference

After grouping, sensitivity analysis is performed using representative sampling. Instead of inspecting every object, the platform selects a small, statistically meaningful subset of files from each dataset.

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Sampling strategies account for factors such as:

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• Partition structure
  
• File size and format
  
• Schema variation within the dataset
  
  

By analyzing these samples, the platform can accurately infer the presence of sensitive data across the entire dataset. This approach preserves accuracy while dramatically reducing the amount of data that must be scanned.

Handling Non-Standard Storage Layouts

In some environments, storage layouts may follow unconventional or highly customized naming schemes that automated grouping cannot fully interpret. In these cases, manual grouping provides additional precision. Security analysts can define logical dataset boundaries, often supported by LLM-assisted analysis to better understand complex or ambiguous structures. Once defined, the same sampling and inference mechanisms are applied, ensuring consistent sensitivity assessment even in edge cases.

Scalability, Cost, and Operational Impact

By combining structural analysis, grouping, and representative sampling, this approach enables:

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• Scalable data discovery across millions or billions of objects
  
• Predictable and significantly reduced cloud scanning costs
  
• Faster onboarding and continuous visibility as data changes
  
• High confidence sensitivity models without exhaustive inspection
  
  

This model aligns with the realities of modern cloud environments, where data volume and velocity continue to increase.

From Discovery to Classification and Continuous Risk Management

Dataset-level asset discovery forms the foundation for scalable classification, access governance, and risk detection. Once assets are defined at the dataset level, classification becomes more accurate and easier to maintain over time. This enables downstream use cases such as identifying over-permissioned access, detecting risky data exposure, and managing AI-driven data access patterns.

Applying These Principles in Practice

Platforms like Sentra apply these principles to help organizations discover, classify, and govern sensitive data at cloud scale - without relying on full object-level scans. By focusing on dataset-level discovery and intelligent sampling, Sentra enables continuous visibility into sensitive data while keeping costs and operational overhead under control.

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Managing access to sensitive information is becoming one of the most critical challenges for organizations in 2026. As data sprawls across cloud platforms, SaaS applications, and on-premises systems, enterprises face compliance violations, security breaches, and operational inefficiencies. Data Access Governance Tools provide automated discovery, classification, and access control capabilities that ensure only authorized users interact with sensitive data. This article examines the leading platforms, essential features, and implementation strategies for effective data access governance.

Best Data Access Governance Tools

The market offers several categories of solutions, each addressing different aspects of data access governance. Enterprise platforms like Collibra, Informatica Cloud Data Governance, and Atlan deliver comprehensive metadata management, automated workflows, and detailed data lineage tracking across complex data estates.

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Specialized Data Access Governance (DAG) platforms focus on permissions and entitlements. Varonis, Immuta, and Securiti provide continuous permission mapping, risk analytics, and automated access reviews. Varonis identifies toxic combinations by discovering and classifying sensitive data, then correlating classifications with access controls to flag scenarios where high-sensitivity files have overly broad permissions.

User Reviews and Feedback

Varonis

• Detailed file access analysis and real-time protection capabilities
• Excellent at identifying toxic permission combinations
• Learning curve during initial implementation

BigID

• AI-powered classification with over 95% accuracy
• Handles both structured and unstructured data effectively
• Strong privacy automation features
• Technical support response times could be improved

OneTrust

• User-friendly interface and comprehensive privacy management
• Deep integration into compliance frameworks
• Robust feature set requires organizational support to fully leverage

Sentra

• Effective data discovery and automation capabilities (January 2026 reviews)
• Significantly enhances security posture and streamlines audit processes
• Reduces cloud storage costs by approximately 20%

Critical Capabilities for Modern Data Access Governance

Effective platforms must deliver several core capabilities to address today's challenges:

Unified Visibility

Tools need comprehensive visibility across IaaS, PaaS, SaaS, and on-premises environments without moving data from its original location. This "in-environment" architecture ensures data never leaves organizational control while enabling complete governance.

Dynamic Data Movement Tracking

Advanced platforms monitor when sensitive assets flow between regions, migrate from production to development, or enter AI pipelines. This goes beyond static location mapping to provide real-time visibility into data transformations and transfers.

Automated Classification

Modern tools leverage AI and machine learning to identify sensitive data with high accuracy, then apply appropriate tags that drive downstream policy enforcement. Deep integration with native cloud security tools, particularly Microsoft Purview, enables seamless policy enforcement.

Toxic Combination Detection

Platforms must correlate data sensitivity with access permissions to identify scenarios where highly sensitive information has broad or misconfigured controls. Once detected, systems should provide remediation guidance or trigger automated actions.

Infrastructure and Integration Considerations

Deployment architecture significantly impacts governance effectiveness. Agentless solutions connecting via cloud provider APIs offer zero impact on production latency and simplified deployment. Some platforms use hybrid approaches combining agentless scanning with lightweight collectors when additional visibility is required.

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Open Source Data Governance Tools

Organizations seeking cost-effective or customizable solutions can leverage open source tools. Apache Atlas, originally designed for Hadoop environments, provides mature governance capabilities that, when integrated with Apache Ranger, support tag-based policy management for flexible access control.

DataHub, developed at LinkedIn, features AI-powered metadata ingestion and role-based access control. OpenMetadata offers a unified metadata platform consolidating information across data sources with data lineage tracking and customized workflows.

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While open source tools provide foundational capabilities, metadata cataloging, data lineage tracking, and basic access controls, achieving enterprise-grade governance typically requires additional customization, integration work, and infrastructure investment. The software is free, but self-hosting means accounting for operational costs and expertise needed to maintain these platforms.

Understanding the Gartner Magic Quadrant for Data Governance Tools

Gartner's Magic Quadrant assesses vendors on ability to execute and completeness of vision. For data access governance, Gartner examines how effectively platforms define, automate, and enforce policies controlling user access to data.

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For organizations invested in Microsoft 365, Purview and Copilot now sit at the center of both data protection and productivity. Purview offers rich DLP capabilities, along with sensitivity labels that drive encryption, retention, and policy. Copilot promises to unlock new value from content in SharePoint, OneDrive, Teams, and other services.

But there is a catch. Both Purview DLP and Copilot depend heavily on labels and correct classification.

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If labels are missing, wrong, or inconsistent, then:

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• DLP rules fire in the wrong places (creating false positives) or miss critical data (worse!).
• Copilot accesses content you never intended it to see and can inadvertently surface it in responses.

In many environments, that’s exactly what’s happening. Labels are applied manually. Legacy content, exports from non‑Microsoft systems, and AI‑ready datasets live side by side with little or no consistent tagging. Purview has powerful controls, it just doesn’t always have the accurate inputs it needs.

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The fastest way to boost performance of Purview DLP and make Copilot safe is to fix labels at the source using a DSPM platform, then let Microsoft’s native controls do the work they’re already good at.

The Limits of M365‑Only Classification

Purview’s built-in classifiers understand certain patterns and can infer sensitivity from content inside the Microsoft 365 estate. That can be useful, but it doesn’t solve two big problems.

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First, PHI, PCI, PII, and IP often originate in systems outside of M365; core banking platforms, claims systems, Snowflake, Databricks, and third‑party SaaS applications. When that data is exported or synced into SharePoint, OneDrive, or Teams, it often arrives without accurate labels.

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Second, even within M365, there are years of accumulated documents, emails, and chat history that have never been systematically classified. Applying labels retroactively is time‑consuming and error‑prone if you rely on manual tagging or narrow content rules. And once there, without contextual analysis and deeper understanding of the unstructured files in which the data lives, it becomes extremely difficult to apply precise sensitivity labels.When you add Copilot (or any AI agent/assistant) into the mix, any mislabeling or blind spots in classification can quickly turn into AI‑driven data exposure. The stakes are higher, and so is the need for a more robust foundation.

Using DSPM to Fix Labels at the Source

A DSPM platform like Sentra plugs into your environment at a different layer. It connects not just to Microsoft 365, but also to cloud providers, data warehouses, SaaS applications, collaboration tools, and AI platforms. It then builds a cross‑environment view of where sensitive data lives and what it contains, based on multi‑signal, AI‑assisted classification that’s tuned to your business context.

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Once it has that view, Sentra can automatically apply or correct Microsoft Purview Information Protection (MPIP) labels across M365 content and, where appropriate, back into other systems. Instead of relying on spotty manual tagging and local heuristics, you get labels that reflect a consistent, enterprise‑wide understanding of sensitivity.

Those labels become the language that Purview DLP, encryption, retention, and Copilot controls understand. You are effectively giving Microsoft’s native tools a richer, more accurate map of your data, enabling them to confidently apply appropriate controls and streamline remediations.

Making Purview DLP Work Smarter

When labels are trustworthy, Purview DLP policies become easier to design and maintain. Rather than creating sprawling rule sets that combine patterns, locations, and exceptions, you can express policies in simple, label‑centric terms:

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• “Encrypt and allow PHI sent to approved partners; block PHI sent anywhere else.”
• “Block Highly Confidential documents shared with external accounts; prompt for justification when Internal documents leave the tenant.”

DSPM’s role is to ensure that content carrying PHI or other regulated data is actually labeled as such, whether it started life in M365 or came from elsewhere. Purview then enforces DLP based on those labels, with far fewer false positives and far fewer edge cases. During rollout, you can run new label‑driven policies in audit mode to observe how they would behave, work with business stakeholders to adjust where necessary, and then move the most critical rules into full enforcement.

Keeping Copilot Inside the Guardrails

Copilot adds another dimension to this story. By design, it reads and reasons over large swaths of your content, then generates responses or summaries based on that content. If you don’t control what Copilot can see, it may surface PHI in a chat about scheduling, or include sensitive IP in a generic project update.

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Here again, labels should be the control plane. Once DSPM has ensured that sensitive content is labeled accurately and consistently, you can use those labels to govern Copilot:

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• Limit Copilot’s access to certain labels or sites, especially those holding PHI, PCI, or trade secrets.
• Restrict certain operations (such as summarization or sharing) when output would be based on Highly Confidential content.
• Exclude specific labeled datasets from Copilot’s index entirely.

Because DSPM also tracks where labeled data moves, it can alert you when sensitive content is copied into a location with different Copilot rules. That gives you an opportunity to remediate before an incident, rather than discovering the issue only after a problematic AI response.

A Practical Path for Microsoft‑Centric Organizations

For organizations that have standardized on Microsoft 365, the message is not “replace Purview” or “turn off Copilot.” It’s to recognize that Purview and Copilot need a stronger foundation of data intelligence to act safely and predictably.

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That foundation comes from pairing DSPM and auto‑labeling with Purview’s native capabilities, which combined enable you to:

1. Discover and classify sensitive data across your full estate, including non‑Microsoft sources.
2. Auto‑apply MPIP labels so that M365 content is tagged accurately and consistently.
3. Simplify DLP and Copilot policies to be label‑driven rather than pattern‑driven.
4. Iterate in audit mode before expanding enforcement.

Once labels are fixed at the source, you can lean on Purview DLP and Copilot with much more confidence. You’ll spend less time chasing noisy alerts and unexpected AI behavior, and more time using the Microsoft ecosystem the way it was intended: as a powerful, integrated platform for secure productivity.

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Ready to supercharge Purview DLP and make M365 Copilot safe by fixing labels at the source? Schedule a Sentra demo.

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As organizations navigate the complexities of cloud environments and AI adoption, the need for robust data classification has never been more critical. With sensitive data sprawling across IaaS, PaaS, SaaS platforms, and on-premise systems, enterprises require tools that can discover, classify, and govern data at scale while maintaining compliance with evolving regulations. The best data classification tools not only identify where sensitive information resides but also provide context around data movement, access controls, and potential exposure risks. This guide examines the leading solutions available today, helping you understand which platforms deliver the accuracy, automation, and integration capabilities necessary to secure your data estate.

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What Are Data Classification Tools?

Data classification tools are specialized platforms designed to automatically discover, categorize, and label sensitive information across an organization's entire data landscape. These solutions scan structured and unstructured data, from databases and file shares to cloud storage and SaaS applications, to identify content such as personally identifiable information (PII), financial records, intellectual property, and regulated data subject to compliance frameworks like GDPR, HIPAA, or CCPA.

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Effective data classification tools leverage machine learning algorithms, pattern matching, metadata analysis, and contextual awareness to tag data accurately. Beyond simple discovery, these platforms correlate classification results with access controls, data lineage, and risk indicators, enabling security teams to identify "toxic combinations" where highly sensitive data sits behind overly permissive access settings. This contextual intelligence transforms raw classification data into actionable security insights, helping organizations prevent data breaches, meet compliance obligations, and establish the governance guardrails necessary for secure AI adoption.

Top Data Classification Tools

Sentra

Sentra is a cloud-native data security platform specifically designed for AI-ready data governance. Unlike legacy classification tools built for static environments, Sentra discovers and governs sensitive data at petabyte scale inside your own environment, ensuring data never leaves your control.

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What Users Like:

• Classification accuracy and contextual risk insights consistently praised in January 2026 reviews
• Speed and precision of classification engine described as unmatched
• DataTreks capability creates interactive maps tracking data movement, duplication, and transformation
• Distinguishes between real sensitive data and mock data to prevent false positives

Key Capabilities:

• Unified visibility across IaaS, PaaS, SaaS, and on-premise file shares without moving data
• Deep Microsoft integration leveraging Purview Information Protection with 95%+ accuracy
• Identifies toxic combinations by correlating data sensitivity with access controls
• Tracks data movement to detect when sensitive assets flow into AI pipelines
• Eliminates shadow and ROT data, typically reducing cloud storage costs by ~20%

BigID

BigID uses AI-powered discovery to automatically identify sensitive or regulated information, continuously monitoring data risks with a strong focus on privacy compliance and mapping personal data across organizations.

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What Users Like:

• Exceptional data classification capabilities highlighted in January 2026 reviews
• Comprehensive data-discovery features for privacy, protection, and governance
• Broad source connectivity across diverse data environments

Varonis

Varonis specializes in unstructured data classification across file servers, email, and cloud content, providing strong access monitoring and insider threat detection.

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What Users Like:

• Detailed file access analysis and real-time protection
• Actionable insights and automated risk visualization

Considerations:

• Learning curve when dealing with comprehensive capabilities

Microsoft Purview

Microsoft Purview delivers exceptional integration for organizations invested in the Microsoft ecosystem, automatically classifying and labeling data across SharePoint, OneDrive, and Microsoft 365 with customizable sensitivity labels and comprehensive compliance reporting.

Nightfall AI

Nightfall AI stands out for real-time detection capabilities across modern SaaS and generative AI applications, using advanced machine learning to prevent data exfiltration and secret sprawl in dynamic environments.

Other Notable Solutions

Forcepoint takes a behavior-based approach, combining context and user intent analysis to classify and protect data across cloud, network, and endpoints, though its comprehensive feature set requires substantial tuning and comes with a steeper learning curve.

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Google Cloud DLP excels for teams pursuing cloud-first strategies within Google's environment, offering machine-learning content inspection that scales seamlessly but may be less comprehensive across broader SaaS portfolios.

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Atlan functions as a collaborative data workspace emphasizing metadata management, automated tagging, and lineage analysis, seamlessly connecting with modern data stacks like Snowflake, BigQuery, and dbt.

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Collibra Data Intelligence Cloud employs self-learning algorithms to uncover, tag, and govern both structured and unstructured data across multi-cloud environments, offering detailed reporting suited to enterprises requiring holistic data discovery with strict compliance oversight.

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Informatica leverages AI to profile and classify data while providing end-to-end lineage visualization and analytics, ideal for large, distributed ecosystems demanding scalable data quality and governance.

Evaluation Criteria for Data Classification Tools

Selecting the right data classification tool requires careful assessment across several critical dimensions:

Classification Accuracy

The engine must reliably distinguish between genuine sensitive data and mock or test data to prevent false positives that create alert fatigue and waste security resources. Advanced solutions employ multiple techniques including pattern matching, proximity analysis, validation algorithms, and exact data matching to improve precision.

Platform Coverage

The best solutions scan IaaS, PaaS, SaaS, and on-premise file shares without moving data from its original location, using metadata collection and in-environment scanning to maintain data sovereignty while delivering centralized governance. This architectural approach proves especially critical for organizations subject to strict data residency requirements.

Automation and Integration

Look for tools that automatically tag and label data based on classification results, integrate with native platform controls (such as Microsoft Purview labels or Snowflake masking policies), and trigger remediation workflows without manual intervention. The depth of integration with your existing technology stack determines how seamlessly classification insights translate into enforceable security policies.

Data Movement Tracking

Modern tools must monitor how sensitive assets flow between regions, migrate across environments (production to development), and feed into AI systems. This dynamic visibility enables security teams to detect risky data transfers before they result in compliance violations or unauthorized exposure.

Scalability and Performance

Evaluate whether the solution can handle your data volume without degrading scan performance or requiring excessive infrastructure resources. Consider the platform's ability to identify toxic combinations, correlating high-sensitivity data with overly permissive access controls to surface the most critical risks requiring immediate remediation.

Best Free Data Classification Tools

For organizations seeking to implement data classification without immediate budget allocation, two notable free options merit consideration:

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Imperva Classifier: Data Classification Tool is available as a free download (requiring only email submission for installation access) and supports multiple operating systems including Windows, Mac, and Linux. It features over 250 built-in search rules for enterprise databases such as Oracle, Microsoft SQL, SAP Sybase, IBM DB2, and MySQL, making it a practical choice for quickly identifying sensitive data at risk across common database platforms.

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Apache Atlas represents a robust open-source alternative originally developed for the Hadoop ecosystem. This enterprise-grade solution offers comprehensive metadata management with dedicated data classification capabilities, allowing organizations to tag and categorize data assets while supporting governance, compliance, and data lineage tracking needs.

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While free tools offer genuine value, they typically require more in-house expertise for customization and maintenance, may lack advanced AI-powered classification engines, and often provide limited support for modern cloud and SaaS environments. For enterprises with complex, distributed data estates or strict compliance requirements, investing in a commercial solution often proves more cost-effective when factoring in total cost of ownership.

Making the Right Choice for Your Organization

Selecting among the best data classification tools requires aligning platform capabilities with your specific organizational context, data architecture, and security objectives. User reviews from January 2026 provide valuable insights into real-world performance across leading platforms.

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When evaluating solutions, prioritize running proof-of-concept deployments against representative samples of your actual data estate. This hands-on testing reveals how well each platform handles your specific data types, integration requirements, and performance expectations. Develop a scoring framework that weights evaluation criteria according to your priorities, whether that's classification accuracy, automation capabilities, platform coverage, or integration depth with existing systems.

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Consider your organization's trajectory alongside current needs. If AI adoption is accelerating, ensure your chosen platform can discover AI copilots, map their knowledge base access, and enforce granular behavioral guardrails on sensitive data. For organizations with complex multi-cloud environments, unified visibility without data movement becomes non-negotiable. Enterprises subject to strict compliance regimes should prioritize platforms with proven regulatory alignment and automated policy enforcement.

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The data classification landscape in 2026 offers diverse solutions, from free and open-source options suitable for organizations with strong technical teams to comprehensive commercial platforms designed for petabyte-scale, AI-driven environments. By carefully evaluating your requirements against the strengths of leading platforms, you can select a solution that not only secures your current data estate but also enables confident adoption of AI technologies that drive competitive advantage.

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Many security stacks look impressive at first glance. There is a DLP agent on every endpoint, a CASB or SSE proxy watching SaaS traffic, EDR and SIEM for hosts and logs, and perhaps a handful of identity and access governance tools. Yet when a serious incident is investigated, it often turns out that sensitive data moved through a path nobody was really watching, or that multiple tools saw fragments of the story but never connected them.

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The common thread is that most stacks were built around infrastructure, not data. They understand networks, workloads, and log lines, but they don’t share a single, consistent understanding of:

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• What your sensitive data is
• Where it actually lives
• Who and what can access it
• How it moves across cloud, SaaS, and AI systems

To move beyond that, security leaders are converging on a data‑first architecture that brings together four capabilities: DSPM (Data Security Posture Management), DLP (Data Loss Prevention), DAG (Data Access Governance), and DDR (Data Detection & Response) in a unified model.

Clarifying the Roles

At the heart of this architecture is DSPM. DSPM is your data‑at‑rest intelligence layer. It continuously discovers data across clouds, SaaS, on‑prem, and AI pipelines, classifies it, and maps its posture; configurations, locations, access paths, and regulatory obligations. Instead of a static inventory, you get a living view of where sensitive data resides and how risky it is.

DLP sits at the edges of the system. Its job is to enforce policy on data in motion and in use: emails leaving the organization, files uploaded to the web, documents synced to endpoints, content copied into SaaS apps, or responses generated by AI tools. DLP decides whether to block, encrypt, quarantine, or simply log based on policies and the context it receives.

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DAG bridges the gap between “what” and “who.” It’s responsible for least‑privilege access; understanding which human and machine identities can access which datasets, whether they really need that access, and what toxic combinations exist when sensitive data is exposed to broad groups or powerful service accounts.

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DDR closes the loop. It monitors access to and movement of sensitive data in real time, looking for unusual or risky behavior: anomalous downloads, mass exports, unusual cross‑region copies, suspicious AI usage. When something looks wrong, DDR triggers detections, enriches them with data context, and kicks off remediation workflows.

When these four functions work together, you get a stack that doesn’t just warn you about potential issues; it actively reduces your exposure and stops exfiltration in motion.

Why “DSPM vs DLP” Is the Wrong Framing

It’s tempting to think of DSPM and DLP as competing answers to the same problem. In reality, they address different parts of the lifecycle. DSPM shows you what’s at risk and where; DLP controls how that risk can materialize as data moves.

Trying to use DLP as a discovery and classification engine is what leads to the noise and blind spots described in the previous section. Conversely, running DSPM without any enforcement at the edges leaves you with excellent visibility but too little control over where data can go.

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DSPM and DAG reduce your attack surface; DLP and DDR reduce your blast radius. DSPM and DAG shrink the pool of exposed data and over‑privileged identities. DLP and DDR watch the edges and intervene when data starts to move in risky ways.

A Unified, Data‑First Reference Architecture

In a data‑first architecture, DSPM sits at the center, connected API‑first into cloud accounts, SaaS platforms, data warehouses, on‑prem file systems, and AI infrastructure. It continuously updates an inventory of data assets, understands which are sensitive or regulated, and applies labels and context that other tools can use.

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On top of that, DAG analyzes which users, groups, service principals, and AI agents can access each dataset. Over‑privileged access is identified and remediated, sometimes automatically: by tightening IAM roles, restricting sharing, or revoking legacy permissions. The result is a significant reduction in the number of places where a single identity can cause significant damage.

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DLP then reads the labels and access context from DSPM and DAG instead of inferring everything from scratch. Email and endpoint DLP, cloud DLP via SSE/CASB, and even platform‑native solutions like Purview DLP all begin enforcing on the same sensitivity definitions and labels. Policies become more straightforward: “Block Highly Confidential outside the tenant,” “Encrypt PHI sent to external partners,” “Require justification for Customer‑Identifiable data leaving a certain region.”

DDR runs alongside this, monitoring how labeled data actually moves. It can see when a typically quiet user suddenly downloads thousands of PHI records, when a service account starts copying IP into a new data store, or when an AI tool begins interacting with a dataset marked off‑limits. Because DDR is fed by DSPM’s inventory and DAG’s access graph, detections are both higher fidelity and easier to interpret.

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From there, integration points into SIEM, SOAR, IAM/CIEM, ITSM, and AI gateways allow you to orchestrate end‑to‑end responses: open tickets, notify owners, roll back risky changes, block certain actions, or update policies.

Where Sentra Fits

Sentra’s product vision aligns directly with this data‑first model. Rather than treating DSPM, DAG, DDR, and DLP intelligence as separate products, Sentra brings them together into a single, cloud‑native data security platform.

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That means you get:

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• DSPM that discovers and classifies data across cloud, SaaS, on‑prem, and AI
• DAG that maps and rationalizes access to that data
• DDR that monitors sensitive data in motion and detects threats
• Integrations that feed this intelligence into DLP, SSE/CASB, Purview, EDR, and other controls

In other words, Sentra is positioned as the brain of the data‑first stack, giving DLP and the rest of your security stack the insight they need to actually stop exfiltration, not just report on it afterward.

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OpenClaw, previously known as MoltBot, isn't just another cybersecurity story - it's a wake-up call for every organization. With over 150,000 GitHub stars and more than 300,000 users in just two months, OpenClaw’s popularity signals a huge change: autonomous AI agents are spreading quickly and dramatically broadening the attack surface in businesses. This is far beyond the risks of a typical ChatGPT plugin or a staff member pasting data into a chatbot. These agents live on user machines and servers with shell-level access, file system privileges, live memory control, and broad integration abilities, usually outside IT or security’s purview.

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Older perimeter and endpoint security tools weren’t built to find or control agents that can learn, store information, and act independently in all kinds of environments. As organizations face this shadow AI risk, the need for real-time, data-level visibility becomes critical. Enter Data Security Posture Management (DSPM): a way for enterprises to understand, monitor, and respond to the unique threats that OpenClaw and its next-generation kin pose.

What makes OpenClaw different - and uniquely dangerous - for security teams?

OpenClaw runs by setting up a local HTTP server and agent gateway on endpoints. It provides shell access, automates browsers, and links with over 50 messaging platforms. But what really sets it apart is how it combines these features with persistent memory. That means agents can remember actions and data far better than any script or bot before. Palo Alto Networks calls this the 'lethal trifecta': direct access to private data, exposure to untrusted content, communication outside the organization, and persistent memory.

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This risk isn't hypothetical. OpenClaw’s skill ecosystem functions like an unguarded software supply chain. Any third-party 'skill' a user adds to an agent can run with full privileges, opening doors to vulnerabilities that original developers can’t foresee. While earlier concerns focused on employees leaking information to public chatbots, tools like OpenClaw operate quietly at system level, often without IT noticing.

From theory to reality: OpenClaw exploitation is active and widespread

This threat is already real. OpenClaw’s design has exposed thousands of organizations to actual attacks. For instance, CVE-2026-25253 is a severe remote code execution flaw caused by a WebSocket validation error, with a CVSS score of 8.8. It lets attackers compromise an agent with a single click (critical OpenClaw vulnerability).

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Attackers wasted no time. The ClawHavoc malware campaign, for example, spread over 341 malicious 'skills’, using OpenClaw’s official marketplace to push info-stealers and RATs directly into vulnerable environments. Over 21,000 exposed OpenClaw instances have turned up on the public internet, often protected by nothing stronger than a weak password, or no authentication at all. Researchers even found plaintext password storage in the code. The risk is both immediate and persistent.

The shadow AI dimension: why you’re likely exposed

One of the trickiest parts of OpenClaw and MoltBot is how easily they run outside official oversight. Research shows that more than 22% of enterprise customers have found MoltBot operating without IT approval. Agents connect with personal messaging apps, making it easy for employees to use them on devices IT doesn’t manage, creating blind spots in endpoint management.

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This reflects a bigger shift: 68% of employees now access free AI tools using personal accounts, and 57% still paste sensitive data into these services. The risks tied to shadow AI keep rising, and so does the cost of breaches: incidents involving unsanctioned AI tools now average $670,000 higher than those without. No wonder experts at Palo Alto, Straiker, Google Cloud, and Intruder strongly advise enterprises to block or at least closely watch OpenClaw deployments.

Why classic security tools are defenseless - and why DSPM is essential

Despite many advances in endpoint, identity, and network defense, these tools fall short against AI agents such as OpenClaw. Agents often run code with system privileges and communicate independently, sometimes over encrypted or unfamiliar channels. This blinds existing security tools to what internal agent 'skills' are doing or what data they touch and process. The attack surface now includes prompt injection through emails and documents, poisoning of agent memory, delayed attacks, and natural language input that bypasses static scans.

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The missing link is visibility: understanding what data any AI agent - sanctioned or shadow - can access, process, or send out. Data Security Posture Management (DSPM) responds to this by mapping what data AI agents can reach, tracing sensitive data to and from agents everywhere they run. Newer DSPM features such as real-time risk scoring, shadow AI discovery, and detailed flow tracking help organizations see and control risks from AI agents at the data layer (Sentra DSPM for AI agent security).

Immediate enterprise action plan: detection, mapping, and control

Security teams need to move quickly. Start by scanning for OpenClaw, MoltBot, and other shadow AI agents across endpoints, networks, and SaaS apps. Once you know where agents are, check which sensitive data they can access by using DSPM tools with AI agent awareness, such as those from Sentra (Sentra’s AI asset discovery). Treat unauthorized installations as active security incidents: reset credentials, investigate activity, and prevent agents from running on your systems following expert recommendations.

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For long-term defense, add continuous shadow AI tracking to your operations. Let DSPM keep your data inventory current, trace possible leaks, and set the right controls for every workflow involving AI. Sentra gives you a single place to find all agent activity, see your actual AI data exposure, and take fast, business-aware action.

Conclusion

OpenClaw is simply the first sign of what will soon be a string of AI agent-driven security problems for enterprises. As companies use AI more to boost productivity and automate work, the chance of unsanctioned agents acting with growing privileges and integrations will continue to rise. Gartner expects that by 2028, one in four cyber incidents will stem from AI agent misuse - and attacks have already started to appear in the news.

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Success with AI is no longer about whether you use agents like OpenClaw; it’s about controlling how far they reach and what they can do. Old-school defenses can’t keep up with how quickly shadow AI spreads. Only data-focused security, with total AI agent discovery, risk mapping, and ongoing monitoring, can provide the clarity and controls needed for this new world. Sentra's DSPM platform offers precisely that. Take the first steps now: identify your shadow AI risks, map out where your data can go, and make AI agent security a top priority.

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Data Privacy Day is a good reminder for all of us in the tech world: finding sensitive data is only the first step. But in today’s environment, data is constantly moving -across cloud platforms, SaaS applications, and AI workflows. The challenge isn’t just knowing where your sensitive data lives; it’s also understanding who or what can touch it, whether that access is still appropriate, and how it changes as systems evolve.

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I’ve seen firsthand that privacy breaks down not because organizations don’t care, but because access decisions are often disconnected from how data is actually being used. You can have the best policies on paper, but if they aren’t continuously enforced, they quickly become irrelevant.

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Discovery is Just the Beginning

Most organizations start with data discovery. They run scans, identify sensitive files, and map out where data lives. That’s an important first step, and it’s necessary, but it’s far from sufficient. Data is not static. It moves, it gets copied, it’s accessed by humans and machines alike. Without continuously governing that access, all the discovery work in the world won’t stop privacy incidents from happening.

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The next step, and the one that matters most today, is real-time governance. That means understanding and controlling access as it happens. 

Who can touch this data? Why do they have access? Is it still needed? And crucially, how do these permissions evolve as your environment changes?

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Take, for example, a contractor who needs temporary access to sensitive customer data. Or an AI workflow that processes internal HR information. If those access rights aren’t continuously reviewed and enforced, a small oversight can quickly become a significant privacy risk.

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Privacy in an AI and Automation Era

AI and automation are changing the way we work with data, but they also change the privacy equation. Automated processes can move and use data in ways that are difficult to monitor manually. AI models can generate insights using sensitive information without us even realizing it. This isn’t a hypothetical scenario, it’s happening right now in organizations of all sizes.

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That’s why privacy cannot be treated as a once-a-year exercise or a checkbox in an audit report. It has to be embedded into daily operations, into the way data is accessed, used, and monitored. Organizations that get this right build systems that automatically enforce policies and flag unusual access - before it becomes a problem.

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Beyond Compliance: Continuous Responsibility

The companies that succeed in protecting sensitive data are those that treat privacy as a continuous responsibility, not a regulatory obligation. They don’t wait for audits or compliance reviews to take action. Instead, they embed privacy into how data is accessed, shared, and used across the organization.

This approach delivers real results. It reduces risk by catching misconfigurations before they escalate. It allows teams to work confidently with data, knowing that sensitive information is protected. And it builds trust - both internally and with customers because people know their data is being handled responsibly.

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A New Mindset for Data Privacy Day

So this Data Privacy Day, I challenge organizations to think differently. The question is no longer “Do we know where our sensitive data is?” Instead, ask:

“Are we actively governing who can touch our data, every moment, everywhere it goes?”

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In a world where cloud platforms, AI systems, and automated workflows touch nearly every piece of data, privacy isn’t a one-time project. It’s a continuous practice, a mindset, and a responsibility that needs to be enforced in real time.

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Organizations that adopt this mindset don’t just meet compliance requirements, they gain a competitive advantage. They earn trust, strengthen security, and maintain a dynamic posture that adapts as systems and access needs evolve.

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Because at the end of the day, true privacy isn’t something you achieve once a year. It’s something you maintain every day, in every process, with every decision. This Data Privacy Day, let’s commit to moving beyond discovery and audits, and make continuous data privacy the standard.

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Discover  What DSPM Vendors Try to Hide 

Your goal in running a data security/DSPM POV is to evaluate all important performance and cost parameters so you can make the best decision and avoid unpleasant surprises. Vendors, on the other hand, are looking for a ‘quick win’ and will often suggest shortcuts like using a limited test data set and copying your data to their environment.

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 On the surface this might sound like a reasonable approach, but if you don’t test real data types and volumes in your own environment, the POV process may hide costly failures or compliance violations that will quickly become apparent in production. A recent evaluation of Sentra versus another top emerging DSPM exposed how the other solution’s performance dropped and costs skyrocketed when deployed at petabyte scale. Worse, the emerging DSPM removed data from the customer environment - a clear controls violation.

If you want to run a successful POV and avoid DSPM buyers' remorse you need to look out for these "dirty little secrets".

Dirty Little Secret #1:
‘Start small’ can mean ‘fails at scale’

The biggest 'dirty secret' is that scalability limits are hidden behind the 'start small' suggestion. Many DSPM platforms cannot scale to modern petabyte-sized data environments. Vendors try to conceal this architectural weakness by encouraging small, tightly scoped POVs that never stress the system and create false confidence. Upon broad deployment, this weakness is quickly exposed as scans slow and refresh cycles stretch, forcing teams to drastically reduce scope or frequency. This failure is fundamentally architectural, lacking parallel orchestration and elastic execution, proving that the 'start small' advice was a deliberate tactic to avoid exposing the platform’s inevitable bottleneck.In a recent POV, Sentra successfully scanned 10x more data in approximately the same time than the alternative:

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Dirty Little Secret #2:
High cloud cost breaks continuous security

Another reason some vendors try to limit the scale of POVs is to hide the real cloud cost of running them in production. They often use brute-force scanning that reads excessive data, consumes massive compute resources, and is architecturally inefficient. This is easy to mask during short, limited POVs, but quickly drives up cloud bills in production. The resulting cost pressure forces organizations to reduce scan frequency and scope, quietly shifting the platform from continuous security control to periodic inventory. Ultimately, tools that cannot scale scanners efficiently on-demand or scan infrequently trade essential security for cost, proving they are only affordable when they are not fully utilized. In a recent POV run on 100 petabytes of data, Sentra proved to be 10x more operationally cost effective to run:

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Dirty Little Secret #3:
‘Good enough’ accuracy degrades security

Accuracy is fundamental to Data Security Posture Management (DSPM) and should not be compromised. While a few points difference may not seem like a deal breaker, every percentage point of classification accuracy can dramatically affect all downstream security controls. Costs increase as manual intervention is required to address FPs. When organizations automate controls based on these inaccuracies, the DSPM platform becomes a source of risk. Confidence is lost. The secret is kept safe because the POV never validates the platform's accuracy against known sensitive data.

In a recent POV Sentra was able to prove less than one percent rate of false positives and false negatives:

DSPM POV Red Flags 

• Copy data to the vendor environment for a “quick win”
  
• Limit features or capabilities to simplify testing
  
• Artificially reduce the size of scanned data
  
• Restrict integrations to avoid “complications”
  
• Limit or avoid API usage
  

These shortcuts don’t make a POV easier - they make it misleading.

Four DSPM POV Requirements That Expose the Truth

If you want a DSPM POV that reflects production reality, insist on these requirements:

1. Scalability

‍Run discovery and classification on at least 1 petabyte of real data, including unstructured object storage. Completion time must be measured in hours or days - not weeks.

2. Cost Efficiency

‍Operate scans continuously at scale and measure actual cloud resource consumption. If cost forces reduced frequency or scope, the model is unsustainable.

3. Accuracy

‍Validate results against known sensitive data. Measure false positives and false negatives explicitly. Accuracy must be quantified and repeatable.

4. Unstructured Data Depth
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Test long-form, heterogeneous, real-world unstructured data including audio, video, etc. Classification must demonstrate contextual understanding, not just keyword matches.

A DSPM solution that only performs well in a limited POV will lead to painful, costly buyer’s regret. Once in production, the failures in scalability, cost efficiency, accuracy, and unstructured data depth quickly become apparent.

Getting ready to run a DSPM POV? Schedule a demo.

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Fintech leaders, from digital-first banks to API-driven investment platforms, face a major data dilemma today. With cloud-native architectures, real-time analytics, and the rapid integration of AI, the scale, speed, and complexity of sensitive data have skyrocketed. Fintech platforms are quickly surpassing what legacy Data Loss Prevention (DLP) and Data Security Posture Management (DSPM) tools can handle.

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Why? Fintech companies now need more than surface-level safeguards. They require true depth: AI-driven data classification, dynamic masking, and fluid integrations across a massive tech stack that includes Snowflake, AWS Bedrock, and Microsoft 365. Below, we look at why DSPM in financial services is at a defining moment, what recurring pain points exist with traditional, and even many emerging, tools, and how Sentra is reimagining what the modern data protection stack should deliver.

The Pitfalls of Legacy DLP and Early DSPM in Fintech

Legacy DLP wasn’t built for fintech’s speed or expanding data footprint. These tools focus on rigid rules and tight boundaries, which aren’t equipped to handle petabyte-scale, multi-cloud, or AI-powered environments. Early DSPM tools brought some improvements in visibility, but problems persisted: incomplete data discovery, basic classification, lots of manual steps, and limited support for dynamic masking.

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For fintech companies, this creates mounting regulatory risk as compliance pressures rise, and slow, manual processes lead to both security and operational headaches. Teams waste hours juggling alerts and trying to piece together patchwork fixes, often resorting to clunky add-on masking tools. The cost is obvious: a scattered protection strategy, long breach response times, and constant exposure to regulatory issues - especially as environments get more distributed and complex.

Why "Good Enough" DSPM Isn’t Enough Anymore

Change in fintech moves faster than ever. The DSPM for the financial services sector is growing at breakneck speed. But as financial applications get more sophisticated, and with cloud and AI adoption soaring, the old "good enough" DSPM falls short. Sensitive data is everywhere now. 82% percent of breaches happen in the cloud, with 39% stretching across multi-cloud or hybrid setups according to The Future of Data Security: Why DSPM is Here to Stay. Enterprise data is set to exceed 181 zettabytes by 2025, raising the stakes for automation, real-time classification, and tight integration with core infrastructure.

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AI and automation are no longer optional. To effectively reduce risk and keep compliance manageable and truly auditable, DSPM systems need to automate classification, masking, remediation, and reporting as a central part of operations, not as last-minute additions.

Where Most DSPM Solutions Fall Short

Fintech organizations often struggle to scale legacy or early DSPM and DLP products, especially those similar to emerging DSPM or large CNAPP vendors. These tools might offer broad control and AI-powered classification, but they usually require too much manual orchestration to achieve full remediation, only automate certain pieces of the workflow, and rely on separate masking add-ons.

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That leads to gaps in AI and multi-cloud data context, choppy visibility, and much of the workflow stuck in manual gear, a recipe for persistent exposure of sensitive data, especially in fast-moving fintech environments.

Fintech buyers, especially those scaling quickly, also point to a crucial need: ensuring DSPM tools natively and deeply support platforms like Snowflake, AWS Bedrock, and Macie. They want automated, business-driven policy enforcement without constantly babysitting the system.

Sentra’s Next-Gen DSPM: AI-Native, Masking-Aware, and Stack-Integrated for Fintech

Sentra was created with these modern fintech challenges in mind. It offers real-time, continuous, agentless classification and deep context for cloud, SaaS, and AI-powered environments.

What makes Sentra different?

• Petabyte-scale agentless discovery: Always-on, friction-free classification, with no heavy infrastructure or manual tweaks.
• AI-native contextualization: Pinpoints sensitive data at a business level and connects instantly with masking policies across Snowflake, Microsoft Purview, and more inferred masking synergy.
• Automation-driven compliance: Handles everything from discovery to masking to changing permissions, with clear, auditable reporting automated masking/remediation.
• Integrated for modern stacks: Ready-made, with out-of-the-box connections for Snowflake, Bedrock, Microsoft 365, and the wider AWS/fintech ecosystem.

More and more fintech companies are switching to Sentra DSPM to achieve true cross-cloud visibility and meet regulations without slowing down. By plugging into fintech data flows and covering AI model pipelines, Sentra lets organizations use DSPM with the same speed as their business.

Building a Future-Ready DSPM Strategy in Financial Services

Managing and protecting sensitive data is a competitive edge for fintech, not just a security concern. With compliance rising up the agenda - 84% of IT and security leaders now list it as a top driver - your DSPM investments need to focus on automation, consistent visibility, and enforceable policies throughout your architecture.

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Next-gen DSPM means: less busywork, no more juggling between masking and classification tools, and instant, actionable insight into data risk, wherever your information lives. In other words, you spend less time firefighting, move faster, and can assure partners and customers that their data is in good hands.

See How SoFi

Request a demo and technical assessment to discover how Sentra’s AI-aware DSPM can speed up both your compliance and your innovation.

Conclusion

Legacy data protection simply can’t keep up with the size, complexity, and regulatory demands of financial data today. DSPM is now table stakes - as long as it’s automated, built with AI at its core, and actively reduces risk in real time, not just points it out.

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Sentra helps you move forward confidently: always-on, agentless classification, automated fixes and masking, and deep stack integration designed for the most complex fintech systems. As you build the future of financial services, your DSPM should make it easier to stay compliant, agile, and protected - no matter how quickly your technology changes.

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Cloud security in 2026 is about protecting sensitive data, identities, and workloads across increasingly complex cloud and multi-cloud environments. As organizations continue moving critical systems to the cloud, security challenges have shifted from basic perimeter defenses to visibility gaps, identity risk, misconfigurations, and compliance pressure. Following proven cloud security best practices helps organizations reduce risk, prevent data exposure, and maintain continuous compliance as cloud environments scale and evolve.

Cloud Security 101

At its core, cloud security aims to protect the confidentiality, integrity, and availability of data and services hosted in cloud environments. This requires a clear grasp of the shared responsibility model, where cloud providers secure the underlying physical infrastructure and core services, while customers remain responsible for configuring settings, protecting data and applications, and managing user access.

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Understanding how different service models affect your level of control is crucial:

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• Software as a Service (SaaS): Provider manages most security controls; you manage user access and data
• Platform as a Service (PaaS): Shared responsibility for application security and data protection
• Infrastructure as a Service (IaaS): You control most security configurations, from OS to applications

Modern cloud security demands cloud-native strategies and automation. Leveraging tools like infrastructure as code, Cloud Security Posture Management (CSPM), and Cloud Workload Protection Platforms helps organizations keep pace with the dynamic, scalable nature of cloud environments. Integrating security into the development process through a "shift left" approach enables teams to detect and remediate vulnerabilities early, before they reach production.

Cloud Security Tips for Beginners

For those new to cloud security, starting with foundational practices builds a strong defense against common threats.

Control Access with Strong Identity Management

• Use multi-factor authentication on every login to add an extra layer of security
• Apply the principle of least privilege by granting users and applications only the permissions they need
• Implement role-based access control across your cloud environment
• Regularly review and audit identity and access policies

Secure Your Cloud Configurations

Regularly audit your cloud settings and use automated tools like CSPM to continuously scan for misconfigurations and risky exposures. Protecting sensitive data requires encrypting information both at rest and in transit using strong standards such as AES-256, ensuring that even if data is intercepted, it remains unreadable. Follow proper key management practices by regularly rotating keys and avoiding hard-coded credentials.

Monitor and Detect Threats Continuously

• Consolidate logs from all cloud services into a centralized system
• Set up real-time monitoring with automated alerts to quickly identify unusual behavior
• Employ behavioral analytics and threat detection tools to continuously assess your security posture
• Develop, document, and regularly test an incident response plan

Security Considerations in Cloud Computing

Before adopting or expanding cloud computing, organizations must evaluate several critical security aspects. First, clearly define which security controls fall under the provider's responsibility versus your own. Review contractual commitments, service level agreements, and compliance with data privacy regulations to ensure data sovereignty and legal requirements are met.

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Data protection throughout its lifecycle is paramount. Evaluate how data is collected, stored, transmitted, and protected with strong encryption both in transit and at rest. Establish robust identity and access controls, including multi-factor authentication and role-based access - to guard against unauthorized access.

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Conducting a thorough pre-migration security assessment is essential:

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• Inventory workloads and classify data sensitivity
• Map dependencies and simulate attack vectors
• Deploy CSPM tools to continuously monitor configurations
• Apply Zero Trust principles—always verify before granting access

Finally, evaluate the provider's internal security measures such as vulnerability management, routine patching, security monitoring, and incident response capabilities. Ensure that both the provider's and your organization's incident response and disaster recovery plans are coordinated, guaranteeing business continuity during security events.

Cloud Security Policies

Organizations should implement a comprehensive set of cloud security policies that cover every stage of data and workload protection.

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Cloud Computing and Cyber Security

Cloud computing fundamentally shifts cybersecurity away from protecting a single, static perimeter toward securing a dynamic, distributed environment. Traditional practices that once focused on on-premises defenses, like firewalls and isolated data centers—must now adapt to an infrastructure where applications and data are continuously deployed and managed across multiple platforms.

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Security responsibilities are now shared between cloud providers and client organizations. Providers secure the core physical and virtual components, while clients must focus on configuring services effectively, managing identity and access, and monitoring for vulnerabilities. This dual responsibility model demands clear communication and proactive management to prevent issues like misconfigurations or exposure of sensitive data.

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The cloud's inherent flexibility and rapid scaling require automated and adaptive security measures. Traditional manual monitoring can no longer keep pace with the speed at which applications and resources are provisioned or updated. Organizations are increasingly relying on AI-driven monitoring, multi-factor authentication, machine learning, and other advanced techniques to continuously detect and remediate threats in real time.

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Cloud environments expand the attack surface by eliminating the traditional network boundary. With data distributed across multiple redundant sites and accessed via numerous APIs, new vulnerabilities emerge that require robust identity- and data-centric protections. Security measures must now encompass everything from strict encryption and access controls to comprehensive logging and incident response strategies that address the unique risks of multi-tenant and distributed architectures. For additional insights on protecting your cloud data, visit our guide on cloud data protection.

Securing Your Cloud Environment with AI-Ready Data Governance

As enterprises increasingly adopt AI technologies in 2026, securing sensitive data while maintaining complete visibility and control has become a critical challenge. Sentra's cloud-native data security platform addresses these challenges by delivering AI-ready data governance and compliance at petabyte scale. Unlike traditional approaches that require data to leave your environment, Sentra discovers and governs sensitive data inside your own infrastructure, ensuring data never leaves your control.

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Cost Savings: By eliminating shadow and redundant, obsolete, or trivial (ROT) data, Sentra not only secures your organization for the AI era but also typically reduces cloud storage costs by approximately 20%.

The platform enforces strict data-driven guardrails while providing complete visibility into your data landscape, where sensitive data lives, how it moves, and who can access it. This "in-environment" architecture replaces opaque data sprawls with a regulator-friendly system that maps data movement and prevents unauthorized AI access, enabling enterprises to confidently adopt AI technologies without compromising security or compliance.

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Implementing effective cloud security tips requires a holistic approach that combines foundational practices with advanced strategies tailored to your organization's unique needs. From understanding the shared responsibility model and securing configurations to implementing robust access controls and continuous monitoring, each element plays a vital role in protecting your cloud environment. As we move further into 2026, the integration of AI-driven security tools, automated governance, and comprehensive data protection measures will continue to define successful cloud security programs. By following these cloud security tips and maintaining a proactive, adaptive security posture, organizations can confidently leverage the benefits of cloud computing while minimizing risk and ensuring compliance with evolving regulatory requirements.

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Introduction: Why Data Access Governance Is Harder Than It Should Be

Data access governance should be simple: know where your sensitive data lives, understand who has access to it, and reduce risk without breaking business workflows. In practice, it’s rarely that straightforward. Modern organizations operate across cloud data stores, SaaS applications, AI pipelines, and hybrid environments. Data moves constantly, permissions accumulate over time, and visibility quickly degrades. Many teams turn to data access governance tools expecting clarity, only to find legacy platforms that are difficult to deploy, noisy, or poorly suited for dynamic, fast-proliferating cloud environments.

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A modern data access governance tool should provide continuous visibility into who and what can access sensitive data across cloud and SaaS environments, and help teams reduce overexposure safely and incrementally.

What Organizations Actually Need from Data Access Governance

Before evaluating vendors, it’s important to align on outcomes, just not features. Most teams are trying to solve the same core problems:

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• Unified visibility across cloud data stores, SaaS platforms, and hybrid environments
• Clear answers to “which identities have access to what, and why?”
• Risk-based prioritization instead of long, unmanageable lists of permissions
• Safe remediation that tightens access without disrupting workflows

Tools that focus only on periodic access reviews or static policies often fall short in dynamic environments where data and permissions change constantly.

Why Legacy and Over-Engineered Tools Fall Short

Many traditional data governance and IGA tools were designed for on-prem environments and slower change cycles. In cloud and SaaS environments, these tools often struggle with:

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• Long deployment timelines and heavy professional services requirements
• Excessive alert noise without clear guidance on what to fix first
• Manual access certifications that don’t scale
• Limited visibility into modern SaaS and cloud-native data stores

Overly complex platforms can leave teams spending more time managing the tool than reducing actual data risk.

Key Capabilities to Look for in a Modern Data Access Governance Tool

1. Continuous Data Discovery and Classification

A strong foundation starts with knowing where sensitive data lives. Modern tools should continuously discover and classify data across cloud, SaaS, and hybrid environments using automated techniques, not one-time scans.

2. Access Mapping and Exposure Analysis

Understanding data sensitivity alone isn’t enough. Tools should map access across users, roles, applications, and service accounts to show how sensitive data is actually exposed.

3. Risk-Based Prioritization

Not all exposure is equal. Effective platforms correlate data sensitivity with access scope and usage patterns to surface the highest-risk scenarios first, helping teams focus remediation where it matters most.

4. Low-Friction Deployment

Look for platforms that minimize operational overhead:

• Agentless or lightweight deployment models
• Fast time-to-value
• Minimal disruption to existing workflows

5. Actionable Remediation Workflows

Visibility without action creates frustration. The right tool should support guided remediation, tightening access incrementally and safely rather than enforcing broad, disruptive changes.

How Teams Are Solving This Today

Security teams that succeed tend to adopt platforms that combine data discovery, access analysis, and real-time risk detection in a single workflow rather than stitching together multiple legacy tools. For example, platforms like Sentra focus on correlating data sensitivity with who or what can actually access it, making it easier to identify over-permissioned data, toxic access combinations, and risky data flows, without breaking existing workflows or requiring intrusive agents.

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The common thread isn’t the tool itself, but the ability to answer one question continuously:

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“Who can access our most sensitive data right now, and should they?”

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Teams using these approaches often see faster time-to-value and more actionable insights compared to legacy systems.

Common Gotchas to Watch Out For

When evaluating tools, buyers often overlook a few critical issues:

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• Hidden costs for deployment, tuning, or ongoing services
• Tools that surface risk but don’t help remediate it
• Point-in-time scans that miss rapidly changing environments
• Weak integration with identity systems, cloud platforms, and SaaS apps

Asking vendors how they handle these scenarios during a pilot can prevent surprises later.
Download The Dirt on DSPM POVs: What Vendors Don’t Want You to Know

How to Run a Successful Pilot

A focused pilot is the best way to evaluate real-world effectiveness:

1. Start with one or two high-risk data stores
2. Measure signal-to-noise, not alert volume
3. Validate that remediation steps work with real teams and workflows
4. Assess how quickly the tool delivers actionable insights

The goal is to prove reduced risk, not just improved reporting.

Final Takeaway: Visibility First, Enforcement Second

Effective data access governance starts with visibility. Organizations that succeed focus first on understanding where sensitive data lives and how it’s exposed, then apply controls gradually and intelligently. Combining DAG with DSPM is an effective way to achieve this.

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In 2026, the most effective data access governance tools are continuous, risk-driven, and cloud-native, helping security teams reduce exposure without slowing the business down.

Frequently Asked Questions (FAQs)

What is data access governance?

‍Data access governance is the practice of managing and monitoring who can access sensitive data, ensuring access aligns with business needs and security requirements.

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How is data access governance different from IAM?

‍IAM focuses on identities and permissions. Data access governance connects those permissions to actual data sensitivity and exposure, and alerts when violations occur.

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How do organizations reduce over-permissioned access safely?

‍By using risk-based prioritization and incremental remediation instead of broad access revocations.

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What should teams look for in a modern data access governance tool?

This question comes up frequently in real-world evaluations, including Reddit discussions where teams share what’s worked and what hasn’t. Teams should prioritize tools that give fast visibility into who can access sensitive data, provide context-aware insights, and allow incremental, safe remediation - all without breaking workflows or adding heavy operational overhead. Cloud- and SaaS-aware platforms tend to outperform legacy or overly complex solutions.

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The cloud has changed how organizations approach data security and compliance. Security leaders have mostly figured out where their sensitive data is, thanks to data security posture management (DSPM) tools. But that's just the beginning. Who can access your data? What are they doing with it?

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Workloads and sensitive assets now move across multi-cloud, hybrid, and SaaS environments, increasing the need for control over access and use. Regulators, boards, and customers expect more than just awareness. They want real proof that you are governing access, lowering risk, and keeping cloud data secure. The next priority is here: shifting from just knowing what data you have to actually governing access to it. Sentra provides a unified platform designed for this shift.

Why Discovery Alone Falls Short in the Cloud Era

DSPM solutions make it possible to locate, classify, and monitor sensitive data almost anywhere, from databases to SaaS apps. This visibility is valuable, particularly as organizations manage more data than ever. Over half of enterprises have trouble mapping their full data environment, and 85% experienced a data loss event in the past year.

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But simply seeing your data won’t do the job. DSPM can point out risks, like unencrypted data or exposed repositories, but it usually can’t control access or enforce policies in real time. Cloud environments change too quickly for static snapshots and scheduled reviews. Effective security means not only seeing your data but actively controlling who can reach it and what they can do.

Data Access Governance: The New Frontier for Cloud Data Security

Data Access Governance (DAG) covers processes and tools that constantly monitor, control, and audit who can access your data, how, and when, wherever it lives in the cloud.

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Why does DAG matter so much now? Consider some urgent needs:

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• Compliance and Auditability: 82% of organizations rank compliance as their top cloud concern. Data access controls and real-time audit logs make it possible to demonstrate compliance with GDPR, HIPAA, and other data laws.
• Risk Reduction: Cloud environments change constantly, so outdated access policies quickly become a problem. DAG enforces least-privilege access, supports just-in-time permissions, and lets teams quickly respond to risky activity.
• AI and New Threats: As generative AI becomes more common, concerns about misuse and unsupervised data access are growing. Forty percent of organizations now see AI as a data leak risk.

DAG gives organizations a current view of “who has access to my data right now?” for both employees and AI agents, and allows immediate changes if permissions or risks shift.

The Power of a Unified, Agentless Platform for DSPM and DAG

Why should security teams look for a unified platform instead of another narrow tool? Most large companies use several clouds, with 83% managing more than one, but only 34% have unified compliance. Legacy tools focused on discovery or single clouds aren’t enough.

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Sentra’s agentless, multi-cloud solution meets these needs directly. With nothing extra to install or maintain, Sentra provides:

• Automated discovery and classification of data in AWS, Azure, GCP, and SaaS
• Real-time mapping and management of every access, from users to services and APIs
• Policy-as-code for dynamic enforcement of least-privilege access
• Built-in detection and response that moves beyond basic rules

This approach combines data discovery with ongoing access management, helping organizations save time and money. It bridges the gaps between security, compliance, and DevOps teams. GlobalNewswire projects the global market for unified data governance will exceed $15B by 2032. Companies are looking for platforms that can keep things simple and scale with growth.

Strategic Benefits: From Reduced Risk to Business Enablement

What do organizations actually achieve with cloud-native, end-to-end data access governance?

• Operational Efficiency: Replace slow, manual reviews and separate tools. Automate access reviews, policy enforcement, and compliance, all in one platform.
• Faster Remediation and Lower TCO: Real-time alerts pinpoint threats faster, and automation speeds up response and reduces resource needs.
• Future-Proof Security: Designed to handle multi-cloud and AI demands, with just-in-time access, zero standing privilege, and fast threat response.
• Business Enablement and Audit Readiness: Central visibility and governance help teams prepare for audits faster, gain customer trust, and safely launch digital products.

In short, a unified platform for DSPM and DAG is more than a tech upgrade, it gives security teams the ability to directly support business growth and agility.

Why Sentra: The Converged Platform for Modern Data Security

Sentra covers every angle: agentless discovery, continuous access control, ongoing threat detection, and compliance, all within one platform. Sentra unites DSPM, DAG, and Data Detection & Response (DDR) in a single solution.

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With Sentra, you can:

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• Stop relying on periodic reviews and move to real-time governance
• See and manage data across all cloud and SaaS services
• Make compliance easier while improving security and saving money

Conclusion

Data discovery is just the first step to securing cloud data. For compliance, resilience, and agility, organizations need to go beyond simply finding data and actually managing who can use it. DSPM isn’t enough anymore, full Data Access Governance is now a must.

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Sentra’s agentless platform gives security and compliance teams a way to find, control, and protect sensitive cloud data, with full oversight along the way. Make the switch now and turn cloud data security into an asset for your business.

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Looking to bring all your cloud data security and access control together? Request a Sentra demo to see how it works, or watch a 5-minute product demo for more on how Sentra helps organizations move from discovery to full data governance.

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As more organizations move sensitive data to the cloud, Data Security Posture Management (DSPM) has become a critical security investment. But as DSPM adoption grows, a big problem is emerging: security teams are overwhelmed by false positives that create too much noise and not enough useful insight. If your security program is flooded with unnecessary alerts, you end up with more risk, not less.

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Most enterprises say their existing data discovery and classification solutions fall short, primarily because they misclassify data. False positives waste valuable analyst time and deteriorate trust in your security operation. Security leaders need to understand what high-quality data classification accuracy really is, why relying only on regex fails, and how to use objective metrics like precision and recall to assess potential tools. Here’s a look at what matters most for accuracy in DSPM.

What Does Good Data Classification Accuracy Look Like?

To make real progress with data classification accuracy, you first need to know how to measure it. Two key metrics - precision and recall - are at the core of reliable classification. Precision tells you the share of correct positive results among everything identified as positive, while recall shows the percentage of actual sensitive items that get caught. You want both metrics to be high. Your DSPM solution should identify sensitive data, such as PII or PCI, without generating excessive false or misclassified results.

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The F1-score adds another perspective, blending precision and recall for a single number that reflects both discovery and accuracy. On the ground, these metrics mean fewer false alerts, quicker responses, and teams that spend their time fixing problems rather than chasing noise. "Good" data classification produces consistent, actionable results, even as your cloud data grows and changes.

The Hidden Cost of Regex-Only Data Discovery

A lot of older DSPM tools still depend on regular expressions (regex) to classify data in both structured and unstructured systems. Regex works for certain fixed patterns, but it struggles with the diverse, changing data types common in today’s cloud and SaaS environments. Regex can't always recognize if a string that “looks” like a personal identifier is actually just a random bit of data. This results in security teams buried by alerts they don’t need, leading to alert fatigue.

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Far from helping, regex-heavy approaches waste resources and make it easier for serious risks to slip through. As privacy regulations become more demanding and the average breach hit $4.4 million according to the annual "Cost of a Data Breach Report" by IBM and the Ponemon Institute, ignoring precision and recall is becoming increasingly costly.

How to Objectively Test DSPM Accuracy in Your POC

If your current DSPM produces more noise than value, a better method starts with clear testing. A meaningful proof-of-value (POV) process uses labeled data and a confusion matrix to calculate true positives, false positives, and false negatives. Don’t rely on vendor promises. Always test their claims with data from your real environment. Ask hard questions: How does the platform classify unstructured data? How much alert noise can you expect? Can it keep accuracy high even when scanning huge volumes across SaaS, multi-cloud, and on-prem systems? The best DSPM tool cuts through the clutter, surfacing only what matters.

Sentra Delivers Highest Accuracy with Small Language Models and Context

Sentra’s DSPM platform raises the bar by going beyond regex, using purpose-built small language models (SLMs) and advanced natural language processing (NLP) for context-driven data classification at scale. Customers and analysts consistently report that Sentra achieves over the highest classification accuracy for PII and PCI, with very few false positives.

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How does Sentra get these results without data ever leaving your environment? The platform combines multi-cloud discovery, agentless install, and deep contextual awareness - scanning extensive environments and accurately discerning real risks from background noise. Whether working with unstructured cloud data, ever-changing SaaS content, or traditional databases, Sentra keeps analysts focused on real issues and helps you stay compliant. Instead of fighting unnecessary alerts, your team sees clear results and can move faster with confidence.

Want to see Sentra DSPM in action? Schedule a Demo.

Reducing False Positives Produces Real Outcomes

Classification accuracy has a direct impact on whether your security is efficient or overwhelmed. With compliance rules tightening and threats growing, security teams cannot afford DSPM solutions that bury them in false positives. Regex-only tools no longer cut it - precision, recall, and truly reliable results should be standard.

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Sentra’s SLM-powered, context-aware classification delivers the trustworthy performance businesses need, changing DSPM from just another alert engine to a real tool for reducing risk. Want to see the difference yourself? Put Sentra’s accuracy to the test in your own environment and finally move past false positive fatigue.

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As enterprises scale cloud usage and adopt AI, the value of Data Security Posture Management (DSPM) is no longer just about checking a tool category box. It’s about protecting what matters most: sensitive data that fuels modern business and AI workflows.

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Traditional content on DSPM often focuses on cost components and deployment considerations. That’s useful, but incomplete. To truly justify DSPM to executives and boards, security leaders need a holistic, outcome-focused view that ties data risk reduction to measurable business impact.

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In this blog, we unpack the real, measurable benefits of DSPM, beyond just cost savings, and explain how modern DSPM strategies deliver rapid value far beyond what most legacy tools promise. 

1. Visibility Isn’t Enough - You Need Context

A common theme in DSPM discussions is that tools help you see where sensitive data lives. That’s important, but it’s only the first step. Real value comes from understanding context. Who can access the data, how it’s being used, and where risk exists in the wider security posture. Organizations that stop at discovery often struggle to prioritize risk and justify spend.

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Modern DSPM solutions go further by:

• Correlating data locations with access rights and usage patterns
• Mapping sensitive data flows across cloud, SaaS, and hybrid environments
• Detecting shadow data stores and unmanaged copies that silently increase exposure
• Linking findings to business risk and compliance frameworks

This contextual intelligence drives better decisions and higher ROI because teams aren’t just counting sensitive data, they’re continuously governing it.

2. DSPM Saves Time and Shrinks Attack Surface Fast

One way DSPM delivers measurable business value is by streamlining functions that used to be manual, siloed, and slow:

• Automated classification reduces manual tagging and human error
• Continuous discovery eliminates periodic, snapshot-alone inventories
• Policy enforcement reduces time spent reacting to audit requests

This translates into:

• Faster compliance reporting
• Shorter audit cycles
• Rapid identification and remediation of critical risks

For security leaders, the speed of insight becomes a competitive advantage, especially in environments where data volumes grow daily and AI models can touch every corner of the enterprise.

3. Cost Benefits That Matter, but with Context

Lately I’m hearing many DSPM discussions break down cost components like scanning compute, licensing, operational expenses, and potential cloud savings. That’s a good start because DSPM can reduce cloud waste by identifying stale or redundant data, but it’s not the whole story.

Here’s where truly strategic DSPM differs:

Operational Efficiency

When DSPM tools automate discovery, classification, and risk scoring:

• Teams spend less time on manual reports
• Alert fatigue drops as noise is filtered
• Engineers can focus on higher-value work

Breach Avoidance

Data breaches are expensive. According to industry studies, the average cost of a data breach runs into millions, far outweighing the cost of DSPM itself. A DSPM solution that prevents even one breach or major compliance failure pays for itself tenfold

Compliance as a Value Center

Rather than treating compliance as a cost center consider that:

• DSPM reduces audit overhead
• Provides automated evidence for frameworks like GDPR, HIPAA, PCI DSS
• Improves confidence in reporting accuracy

That’s a measurable business benefit CFOs can appreciate and boards expect.

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4. DSPM Reduces Risk Vector Multipliers Like AI

One benefit that’s often under-emphasized is how DSPM reduces risk vector multipliers, the factors that amplify risk exponentially beyond simple exposure counts.

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In 2026 and beyond, AI systems are increasingly part of the risk profile. Modern DSPM help reduce the heightened risk from AI by:

• Identifying where sensitive data intersects with AI training or inference pipelines
• Governing how AI tools and assistants can access sensitive content
• Providing risk context so teams can prevent data leakage into LLMs

This kind of data-centric, contextual, and continuous governance should be considered a requirement for secure AI adoption, no compromise.

5. Telling the DSPM ROI Story

The most convincing DSPM ROI stories aren’t spreadsheets, they’re narratives that align with business outcomes. The key to building a credible ROI case is connecting metrics, security impact, and business outcomes:

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By telling the story this way, security leaders can speak in terms the board and executives care about: risk reduction, compliance assurance, operational alignment, and controlled growth.

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How to Evaluate DSPM for Real ROI

To capture tangible return, don’t evaluate DSPM solely on cost or feature checklists. Instead, test for:

1. Scalability Under Real Load

Can the tool discover and classify petabytes of data, including unstructured content, without degrading performance?

2. Accuracy That Holds Up

Poor classification undermines automation. True ROI requires consistent, top-performing accuracy rates.

3. Operational Cost Predictability

Beware of DSPM solutions that drive unexpected cloud expenses due to inefficient scanning or redundant data reads.

4. Integration With Enforcement Workflows

Visibility without action isn’t ROI. Your DSPM should feed DLP, IAM/CIEM, SIEM/SOAR, and compliance pipelines (ticketing, policy automation, alerts).

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ROI Is a Journey, Not a Number

Costs matter, but value lives in context. DSPM is not just a cost center, it’s a force multiplier for secure cloud operations, AI readiness, compliance, and risk reduction. Instead of seeing DSPM as another tool, forward-looking teams view it as a fundamental decision support engine that changes how risk is measured, prioritized, and controlled.

Ready to See Real DSPM Value in Your Environment?

Download Sentra’s “DSPM Dirty Little Secrets” guide, a practical roadmap for evaluating DSPM with clarity, confidence, and production reality in mind.

👉 Download the DSPM Dirty Little Secrets guide now

Want a personalized walkthrough of how Sentra delivers measurable DSPM value?
👉 Request a demo

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For years, security programs have focused on protecting infrastructure: networks, servers, endpoints, and applications. That approach made sense when systems were static and data rarely moved. It’s no longer enough.

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Recent breach data shows a consistent pattern. Organizations detect incidents, restore systems, and close tickets, yet remain unable to answer the most important question regulators and customers often ask:

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Where does my sensitive data reside?

Who or what has access to this data and are they authorized?

Which specific sensitive datasets were accessed or exfiltrated?

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Infrastructure security alone cannot answer that question.

Infrastructure Alerts Detect Events, Not Impact

Most security tooling is infrastructure-centric by design. SIEMs, EDRs, NDRs, and CSPM tools monitor hosts, processes, IPs, and configurations. When something abnormal happens, they generate alerts.

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What they do not tell you is:

• Which specific datasets were accessed
• Whether those datasets contained PHI or PII
• Whether sensitive data was copied, moved, or exfiltrated

Traditional tools monitor the "plumbing" (network traffic, server logs, etc.) While they can flag that a database was accessed by an unauthorized IP, they often cannot distinguish between an attacker downloading a public template or downloading a table containing 50,000 Social Security numbers. An alert is not the same as understanding the exposure of the data stored inside it. Without that context, incident response teams are forced to infer impact rather than determine it.

The “Did They Access the Data?” Problem

This gap becomes pronounced during ransomware and extortion incidents.

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In many cases:

• Operations are restored from backups
• Infrastructure is rebuilt
• Access is reduced
• (Hopefully!) attackers are removed from the environment

Yet organizations still cannot confirm whether sensitive data was accessed or exfiltrated during the dwell time.

Without data-level visibility:

• Legal and compliance teams must assume worst-case exposure
• Breach notifications expand unnecessarily
• Regulatory penalties increase due to uncertainty, not necessarily damage
  
  

The inability to scope an incident accurately is not a tooling failure during the breach, it is a visibility failure that existed long before the breach occurred. Under regulations like GDPR or CCPA/CPRA, if an organization cannot prove that sensitive data wasn’t accessed during a breach, they are often legally required to notify all potentially affected parties. This ‘over-notification’ is costly and damaging to reputation.

Data Movement Is the Real Attack Vulnerability

Modern environments are defined by constant data movement:

• Cloud migrations
• SaaS integrations
• App dev lifecycles
• Analytics and ETL pipelines
• AI and ML workflows

Each transition creates blind spots.

Legacy platforms awaiting migration often exist in a “wait state” with reduced monitoring. Data copied into cloud storage or fed into AI pipelines frequently loses lineage and classification context. Posture may vary and traditional controls no longer apply consistently. From an attacker’s perspective, these environments are ideal. From a defender’s perspective, they are blind spots.

Policies Are Not Proof

Most organizations can produce policies stating that sensitive data is encrypted, access-controlled, and monitored. Increasingly, regulators are moving from point-in-time audits to requiring continuous evidence of control.  

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Regulators are asking for evidence:

• Where does PHI live right now?
• Who or what can access it?
• How do you know this hasn’t changed since the last audit?

Point-in-time audits cannot answer those questions. Neither can static documentation. Exposure and access drift continuously, especially in cloud and AI-driven environments.

Compliance depends on continuous control, not periodic attestation.

What Data-Centric Security Actually Requires

Accurately proving compliance and scoping breach impact requires security visibility that is anchored to the data itself, not the infrastructure surrounding it.

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At a minimum, this means:

• Continuous discovery and classification of sensitive data
• Consistent compliance reporting and controls across cloud, SaaS, On-Prem, and migration states
• Clear visibility into which identities, services, and AI tools can access specific datasets
• Detection and response signals tied directly to sensitive data exposure and movement

This is the operational foundation of Data Security Posture Management (DSPM) and Data Detection and Response (DDR). These capabilities do not replace infrastructure security controls; they close the gap those controls leave behind by connecting security events to actual data impact.

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This is the problem space Sentra was built to address.

Sentra provides continuous visibility into where sensitive data lives, how it moves, and who or what can access it, and ties security and compliance outcomes to that visibility. Without this layer, organizations are forced to infer breach impact and compliance posture instead of proving it.

Why Data-Centric Security Is Required for Today's Compliance and Breach Response

Infrastructure security can detect that an incident occurred, but it cannot determine which sensitive data was accessed, copied, or exfiltrated. Without data-level evidence, organizations cannot accurately scope breaches, contain risk, or prove compliance, regardless of how many alerts or controls are in place. Modern breach response and regulatory compliance require continuous visibility into sensitive data, its lineage, and its access paths. Infrastructure-only security models are no longer sufficient.

Want to see how Sentra provides complete visibility and control of sensitive data?‍

Schedule a Demo

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Preventing data breaches in healthcare is no longer just about stopping cyberattacks. In 2026, the greater challenge is maintaining continuous visibility into where protected health information (PHI) lives, how it is accessed, and how it is reused across modern healthcare environments governed by HIPAA compliance requirements.

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PHI no longer resides in a single system or under the control of one team. It moves constantly between cloud platforms, electronic health record (EHR) systems, business associates, analytics environments, and AI tools used throughout healthcare operations. While this data sharing enables better patient care and operational efficiency, it also introduces new healthcare cybersecurity risks that traditional, perimeter-based security controls were never designed to manage.

From Perimeter Security to Data-Centric PHI Protection

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Many of the most damaging healthcare data breaches in recent years have shared a common root cause:

limited visibility into sensitive data and unclear ownership across shared environments.

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Over-permissioned identities, long-lived third-party access, and AI systems interacting with regulated data without proper governance can silently expand exposure until an incident forces disruptive containment measures. Protecting PHI in 2026 requires a data-centric approach to healthcare data security. Instead of focusing only on where data is stored, organizations must continuously understand what sensitive data exists, who can access it, and how that access changes over time. This shift is foundational to effective HIPAA compliance, resilient incident response, and the safe adoption of AI in healthcare.

The Importance of Data Security in Healthcare

Healthcare organizations continue to face disproportionate risk from data breaches, with incidents carrying significant financial, operational, and reputational consequences. Recent industry analyses show that healthcare remains the costliest industry for data breaches, with the average breach costing approximately $7.4 million globally in 2025 and exceeding $10 million per incident in the U.S., driven by regulatory penalties and prolonged recovery efforts.

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The scale and complexity of healthcare breaches have also increased. As of late 2025, hundreds of large healthcare data breaches affecting tens of millions of individuals had already been reported in the U.S. alone, including incidents tied to shared infrastructure and third-party service providers. These events highlight how a single exposure can rapidly expand across interconnected healthcare ecosystems.

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Importantly, many recent breaches are no longer caused solely by external attacks. Instead, they stem from internal access issues such as over-permissioned identities, misdirected data sharing, and long-lived third-party access, risks now amplified by analytics platforms and AI tools interacting directly with regulated data. As healthcare organizations continue to adopt new technologies, protecting PHI increasingly depends on controlling how sensitive data is accessed, shared, and reused over time, not just where it is stored.

Healthcare Cybersecurity Regulations & Standards

For healthcare organizations, it is especially crucial to protect patient data and follow industry rules. Transitioning to the cloud shouldn't disrupt compliance efforts. But staying on top of strict data privacy regulations adds another layer of complexity to managing healthcare data.
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Below are some of the top healthcare cybersecurity regulations relevant to the industry.

Health Insurance Portability and Accountability Act of 1996 (HIPAA)

HIPAA is pivotal in healthcare cybersecurity, mandating compliance for covered entities and business associates. It requires regular risk assessments and adherence to administrative, physical, and technical safeguards for electronic Protected Health Information (ePHI).

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HIPAA, at its core, establishes national standards to protect sensitive patient health information from being disclosed without the patient's consent or knowledge. For leaders in healthcare data management, understanding the nuances of HIPAA's Titles and amendments is essential. Particularly relevant are Title II's (HIPAA Administrative Simplification), Privacy Rule, and Security Rule.
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HHS 405(d)

HHS 405(d) regulations, under the Cybersecurity Act of 2015, establish voluntary guidelines for healthcare cybersecurity, embodied in the Healthcare Industry Cybersecurity Practices (HICP) framework. This framework covers email, endpoint protection, access management, and more.

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Health Information Technology for Economic and Clinical Health (HITECH) Act

The HITECH Act, enacted in 2009, enhances HIPAA requirements, promoting the adoption of healthcare technology and imposing stricter penalties for HIPAA violations. It mandates annual cybersecurity audits and extends HIPAA regulations to business associates.

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Payment Card Industry Data Security Standard (PCI DSS)

PCI DSS applies to healthcare organizations processing credit cards, ensuring the protection of cardholder data. Compliance is necessary for handling patient card information.

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Quality System Regulation (QSR)

The Quality System Regulation (QSR), enforced by the FDA, focuses on securing medical devices, requiring measures like access prevention, risk management, and firmware updates. Proposed changes aim to align QSR with ISO 13485 standards.

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Health Information Trust Alliance (HITRUST)

HITRUST, a global cybersecurity framework, aids healthcare organizations in aligning with HIPAA guidelines, offering guidance on various aspects including endpoint security, risk management, and physical security. Though not mandatory, HITRUST serves as a valuable resource for bolstering compliance efforts.

Preventing Data Breaches in Healthcare with Sentra

Sentra’s Data Security Posture Management (DSPM) automatically discovers and accurately classifies your sensitive patient data. By seamlessly building a well-organized data catalog, Sentra ensures all your patient data is secure, stored correctly and in compliance. The best part is, your data never leaves your environment.

Discover and Accurately Classify your High Risk Patient Data

Discover and accurately classify your high-risk patient data with ease using Sentra. Within minutes, Sentra empowers you to uncover and comprehend your Protected Health Information (PHI), spanning patient medical history, treatment plans, lab tests, radiology images, physician notes, and more. 

Seamlessly build a well-organized data catalog, ensuring that all your high-risk patient data is securely stored and compliant. As a cloud-native solution, Sentra enables you to scale security across your entire data estate. Your cloud data remains within your environment, putting you in complete control of your sensitive data at all times.

Sentra Reduces Data Risks by Controlling Posture and Access

Sentra is your solution for reducing data risks and preventing data breaches by efficiently controlling posture and access. With Sentra, you can enforce security policies for sensitive data, receiving alerts to violations promptly. It detects which users have access to sensitive Protected Health Information (PHI), ensuring transparency and accountability. Additionally, Sentra helps you manage third-party access risks by offering varying levels of access to different providers. Achieve least privilege access by leveraging Sentra's continuous monitoring and tracking capabilities, which keep tabs on access keys and user identities. This ensures that each user has precisely the right access permissions, minimizing the risk of unauthorized data exposure.

Stay on Top of Healthcare Data Regulations with Sentra

Sentra’s Data Security Posture Management (DSPM) solution streamlines and automates the management of your regulated patient data, preparing you for significant security audits. Gain a comprehensive view of all sensitive patient data, allowing our platform to automatically identify compliance gaps for proactive and swift resolution.

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Easily translate your compliance requirements for HIPAA, GDPR, and HITECH into actionable rules and policies, receiving notifications when data is copied or moved between regions. With Sentra, running compliance reports becomes a breeze, providing you with all the necessary evidence, including sensitive data types, regulatory controls, and compliance status for relevant regulatory frameworks.

Conclusion: From Perimeter Security to Continuous Data Governance

Healthcare organizations can no longer rely on perimeter-based controls or periodic audits to prevent data breaches. As PHI spreads across cloud platforms, business associates, and AI-driven workflows, the risk is no longer confined to a single system, it’s embedded in how data is accessed, shared, and reused.

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Protecting PHI in 2026 requires continuous visibility into sensitive data and the ability to govern it throughout its lifecycle. This means understanding what regulated data exists, who has access to it, and how that access changes over time - across internal teams, third parties, and AI systems. Without this level of insight, compliance with HIPAA and other healthcare regulations becomes reactive, and incident response becomes disruptive by default.

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A data-centric security model allows healthcare organizations to reduce their breach impact, limit regulatory exposure, and adopt AI safely without compromising patient trust. By shifting from static controls to continuous data governance, security and compliance teams can move from guessing where PHI lives to managing it with confidence.

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To learn more about how you can enhance your data security posture, schedule a demo with one of our data security experts.

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After debuting in Gartner’s 2022 Hype Cycle, Data Security Posture Management (DSPM) has quickly become a transformative category and hot security topic. DSPM solutions are popping up everywhere, both as dedicated offerings and as add-on modules to established cloud native application protection platforms (CNAPP) or cloud security posture management (CSPM) platforms.

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But which option is better: adding a DSPM module to one of your existing solutions or implementing a new DSPM-focused platform? On the surface, activating a module within a CNAPP/CSPM solution that your team already uses might seem logical. But, the real question is whether or not you can reap all of the benefits of a DSPM through an add-on module. While some CNAPP platforms offer a DSPM module, these add-ons lack a fully data-centric approach, which is required to make DSPM technology effective for a modern-day business with a sprawling data ecosystem. Let’s explore this further.

How are CNAPP/CSPM and DSPM Different?

While CNAPP/CSPM and DSPM seem similar and can be complementary in many ways, they are distinctly different in a few important ways. DSPMs are all about the data — protecting it no matter where it travels. CNAPP/CSPMs focus on detecting attack paths through cloud infrastructure. So naturally, they tie specifically to the infrastructure and lack the agnostic approach of DSPM to securing the underlying data.

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Because a DSPM focuses on data posture, it applies to additional use cases that CNAPP/CSPM typically doesn’t cover. This includes data privacy and data protection regulations such as GDPR, PCI-DSS, etc., as well as data breach detection based on real-time monitoring for risky data access activity. Lastly, data at rest (such as abandoned shadow data) would not necessarily be protected by CNAPP/CSPM since, by definition, it’s unknown and not an active attack path.

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What is a Data-Centric Approach?

A data-centric approach is the foundation of your data security strategy that prioritizes the secure management, processing, and storage of data, ensuring that data integrity, accessibility, and privacy are maintained across all stages of its lifecycle. Standalone DSPM takes a data-centric approach. It starts with the data, using contextual information such as data location, sensitivity, and business use cases to better control and secure it. These solutions offer preventative measures, such as discovering shadow data, preventing data sprawl, and reducing the data attack surface.

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Data detection and response (DDR), often offered within a DSPM platform, provides reactive measures, enabling organizations to monitor their sensitive assets and detect and prevent data exfiltration. Because standalone DSPM solutions are data-centric, many are designed to follow data across a hybrid ecosystem, including public cloud, private cloud, and on-premises environments. This is ideal for the complex environments that many organizations maintain today.

What is an Infrastructure-Centric Approach?

An infrastructure-centric solution is focused on optimizing and protecting the underlying hardware, networks, and systems that support applications and services, ensuring performance, scalability, and reliability at the infrastructure level. Both CNAPP and CSPM use infrastructure-centric approaches. Their capabilities focus on identifying vulnerabilities and misconfigurations in cloud infrastructure, as well as some basic compliance violations. CNAPP and CSPM can also identify attack paths and use several factors to prioritize which ones your team should remediate first. While both solutions can enforce policies, they can only offer security guardrails that protect static infrastructure. In addition, most CNAPP and CSPM solutions only work with public cloud environments, meaning they cannot secure private cloud or on-premises environments.

How Does a DSPM Add-On Module for CNAPP/CSPM Work?

Typically, when you add a DSPM module to CNAPP/CSPM, it can only work within the parameters set by its infrastructure-centric base solution. In other words, a DSPM add-on to a CNAPP/CSPM solution will also be infrastructure-centric. It’s like adding chocolate chips to vanilla ice cream; while they will change the flavor a bit, they can’t transform the constitution of your dessert into chocolate ice cream. 

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A DSPM module in a CNAPP or CSPM solution generally has one purpose: helping your team better triage infrastructure security issues. Its sole functionality is to look at the attack paths that threaten your public cloud infrastructure, then flag which of these would most likely lead to sensitive data being breached. 

However, this functionality comes with a few caveats. While CSPM and CNAPP have some data discovery capabilities, they use very basic classification functions, such as pattern-matching techniques. This approach lacks context and granularity and requires validation by your security team. 

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In addition, the DSPM add-on can only perform this data discovery within infrastructure already being monitored by the CNAPP/CSPM solution. So, it can only discover sensitive data within known public cloud environments. It may miss shadow data that has been copied to local stores or personal machines, leaving risky exposure gaps.

Why Infrastructure-Centric Solutions Aren’t Enough

So, what happens when you only use infrastructure-centric solutions in a modern cloud ecosystem? While these solutions offer powerful functionality for defending your public cloud perimeter and minimizing misconfigurations, they miss essential pieces of your data estate. Here are a few types of sensitive assets that often slip through the cracks of an infrastructure-centric approach: 

• Data that gets moved or copied from a higher, protected environment to a lower, unmonitored one (for example, from production to development)
• Data that resides in a private cloud or on-prem environment
• Data that is unknown to the security team (i.e., shadow or ghost data)

In addition, DSPM modules within CNAPP/CSPM platforms lack the context to properly classify sensitive data beyond easily identifiable examples, such as social security or credit card numbers. But, the data stores at today’s businesses often contain more nuanced personal or product/service-specific identifiers that could pose a risk if exposed. Examples include a serial number for a product that a specific individual owns or a medical ID number as part of an EHR. Some sensitive assets might even be made up of “toxic combinations,” in which the sensitivity of seemingly innocuous data classes increases when combined with specific identifiers.

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For example, a random 9-digit number alongside a headshot photo and expiration date is likely a sensitive passport number. Ultimately, DSPM built into a CSPM or CNAPP solution only sees an incomplete picture of risk. This can leave any number of sensitive assets unknown and unprotected in your cloud and on-prem environments.

Dedicated DSPM Completes the Data Security Picture

A dedicated, best-of-breed DSPM solution like Sentra, on the other hand, offers rich, contextual information about all of your sensitive data - no matter where it resides, how your business uses it, or how nuanced it is. 

Rather than just defending the perimeters of known public cloud infrastructure, Sentra finds and follows your sensitive data wherever it goes.

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Here are a few of Sentra’s unique capabilities that complete your picture of data security:

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• Comprehensive, security-focused data catalog of all sensitive data assets across the entire data estate (IaaS, PaaS, SaaS, and On-Premises)
• Ability to detect unmanaged, mislocated, or abandoned data, enabling your team to reduce your data attack surface, control data sprawl, and remediate security/privacy policy violations
• Movement detection to surface out-of-policy data transformations that violate residency and security policies or that inadvertently create exposures
• Nuanced discovery and classification, such as row/column/table analysis capabilities that can uncover uncommon personal identifiers, toxic combinations, etc.
• Rich context for understanding the business purpose of data to better discern its level of sensitivity
• Lower false positive rates due to deeper analysis of the context surrounding each sensitive data store and asset
• Automation for remediating a variety of data posture, compliance, and security issues

All of this complex analysis requires a holistic, data-centric view of your data estate - something that only a standalone DSPM solution can offer. And when deployed together with a CNAPP or CSPM solution, a standalone DSPM platform can bring unmatched depth and context to your cloud data security program. It also provides unparalleled insight to facilitate prioritization of issue resolution.

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Why DSPM Is Essential for Modern Data Security

DSPM, CSPM, and CNAPP each play an important role in modern cloud security, but they are designed to solve fundamentally different problems. CSPM and CNAPP focus on securing cloud infrastructure by identifying misconfigurations and attack paths, while DSPM is purpose-built to protect sensitive data itself - regardless of where that data lives or how it moves across environments.

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As organizations manage increasingly complex data estates spanning public cloud, private cloud, SaaS, and on-premises systems, infrastructure-centric security alone is no longer sufficient. Sensitive data, shadow data, and nuanced “toxic combinations” require continuous discovery, contextual classification, and data-centric monitoring that only a dedicated DSPM solution can provide.

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When deployed alongside CSPM or CNAPP, a standalone DSPM platform completes the data security picture by adding deep visibility into data risk, enabling stronger compliance with privacy regulations, and reducing the overall data attack surface. For organizations looking to protect sensitive data at scale, while supporting modern use cases like AI and analytics - DSPM is a critical foundation of an effective enterprise data security strategy.

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To learn more about Sentra’s approach to data security posture management, read about how we use LLMs to classify structured and unstructured sensitive data at scale.

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2025 was a pivotal year for Chief Information Security Officers (CISOs). As cyber threats surged and digital acceleration transformed business, CISOs gained more influence in boardrooms but also took on greater accountability. The old model of perimeter-based defense has ended. Security strategies now focus on resilience and real-time visibility with sensitive data protection at the core.

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As 2026 approaches, CISOs are turning this year’s lessons into a proactive, AI-smart, and business-aligned strategy. This article highlights the top CISO priorities for 2026, the industry’s shift from prevention to resilience, and how Sentra supports security leaders in this new phase.

Lessons from 2025: Transparency, AI Risk, and Platform Resilience

Over the past year, CISOs encountered high-profile breaches and shifting demands. According to the Splunk 2025 CISO Report an impressive 82% reported direct interactions with CEOs, and 83% regularly attended board meetings. Still, only 29% of board members had cybersecurity experience, leading to frequent misalignment around budgets, innovation, and staffing.

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The data is clear: 76% of CISOs expected a significant cyberattack, but 58% felt unprepared, as reported in the Proofpoint 2025 Voice of the CISO Report. Many CISOs struggled with overwhelming tool sprawl and alert fatigue, 76% named these as major challenges. The rapid growth in cloud, SaaS, and GenAI environments left major visibility gaps, especially for unstructured and shadow data. Most of all, CISOs concluded that resilience - quick detection, rapid response, and keeping the business running, matters more than just preventing attacks. This shift is changing the way security budgets will be spent in 2026.

The Evolution of DSPM: From Inventory to Intelligent, AI-Aware Defense

First generation data security posture management (DSPM) tools focused on identifying assets and manually classifying data. Now, CISOs must automatically map, classify, and assign risk scores to data - structured, unstructured, or AI-generated - across cloud, on-prem and SaaS environments, instantly. If organizations lack this capability, critical data remains at risk (Data as the Core Focus in the Cloud Security Ecosystem).

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AI brings both opportunity and risk. CISOs are working to introduce GenAI security policies while facing challenges like data leakage, unsanctioned AI projects, and compliance issues. DSPM solutions that use machine learning and real-time policy enforcement have become essential.

The Top Five CISO Priorities in 2026

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1. Secure and Responsible AI: As AI accelerates across the business, CISOs must ensure it does not introduce unmanaged data risk. The focus will be on maintaining visibility and control over sensitive data used by AI systems, preventing unintended exposure, and establishing governance that allows the company to innovate with AI while protecting trust, compliance, and brand reputation.

2. Modern Data Governance: As sensitive data sprawls across on-prem, cloud, SaaS, and data lakes, CISOs face mounting compliance pressure without clear visibility into where that data resides. The priority will be establishing accurate classification and governance of sensitive, unstructured, and shadow data - not only to meet regulatory obligations, but to proactively reduce enterprise risk, limit blast radius, and strengthen overall security posture.
   
   
3. Tool Consolidation: As cloud and application environments grow more complex, CISOs are under pressure to reduce data sprawl without increasing risk. The priority is consolidating fragmented cloud and application security tools into unified platforms that embed protection earlier in the development lifecycle, improve risk visibility across environments, and lower operational overhead. For boards, this shift represents both stronger security outcomes and a clearer return on security investment through reduced complexity, cost, and exposure.

4. Offensive Security/Continuous Testing: One-time security assessments can no longer keep pace with AI-driven and rapidly evolving threats. CISOs are making continuous offensive security a core risk-management practice, regularly testing environments across hardware, cloud, and SaaS to expose real-world vulnerabilities. For the board, this provides ongoing validation of security effectiveness and reduces the likelihood of unpleasant surprises from unknown exposures. Some exciting new AI red team solutions are appearing on the scene such as 7ai, Mend.io, Method Security, and Veria Labs.

5. Zero Trust Identity Governance: Identity has become the primary attack surface, making advanced governance essential rather than optional. CISOs are prioritizing data-centric, Zero Trust identity controls to limit excessive access, reduce insider risk, and counter AI-enabled attacks. At the board level, this shift is critical to protecting sensitive assets and maintaining resilience against emerging threats.

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These areas show a greater need for automation, better context, and clearer reporting for boards.

Sentra Enables Secure and Responsible AI with Modern Data Governance

As AI becomes central to business strategy, CISOs are being held accountable for ensuring innovation does not outpace security, governance, or trust. Secure and Responsible AI is no longer about policy alone, it requires continuous visibility into the sensitive data flowing into AI systems, control over shadow and AI-generated data, and the ability to prevent unintended exposure before it becomes a business risk.

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At the same time, Modern Data Governance has emerged as a foundational requirement. Exploding data volumes across cloud, SaaS, data lakes, and on-prem environments have made traditional governance models ineffective. CISOs need accurate classification, unified visibility, and enforceable controls that go beyond regulatory checkboxes to actively reduce enterprise risk.

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Sentra brings these priorities together by giving security leaders a clear, real-time understanding of where sensitive data lives, how it is being used - including by AI - and where risk is accumulating across the organization. By unifying DSPM and Data Detection & Response (DDR), Sentra enables CISOs to move from reactive security to proactive governance, supporting AI adoption while maintaining compliance, resilience, and board-level confidence.

Looking ahead to 2026, the CISOs who lead will be those who can see, govern, and secure their data everywhere it exists and ensure it is used responsibly to power the next phase of growth. Sentra provides the foundation to make that possible.

Conclusion

The CISO’s role in 2025 shifted from putting out fires to driving change alongside business leadership. Expectations will keep rising in 2026; balancing board expectations, the opportunities and threats of AI, and constant new risks takes a smart platform and real-time clarity.

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Sentra delivers the foundation and intelligence CISOs need to build resilience, stay compliant, and fuel data-powered AI growth with secure data. Those who can see, secure, and respond wherever their data lives will lead. Sentra is your partner to move forward with confidence in 2026.

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Data Subject Access Requests (DSARs) are one of the most demanding requirements under privacy regulations such as GDPR and CPRA. As personal data spreads across cloud, SaaS, and legacy systems, responding to DSARs manually becomes slow, costly, and error-prone. This article explores why DSARs are so difficult to scale, the key challenges organizations face, and how DSAR automation enables faster, more reliable compliance.

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Privacy regulations are no longer just legal checkboxes, they are a foundation of customer trust. In today’s data-driven world, individuals expect transparency into how their personal information is collected, used, and protected. Organizations that take privacy seriously demonstrate respect for their users, strengthening trust, loyalty, and long-term engagement.

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Among these requirements, DSARs are often the most complex to support. They give individuals the right to request access to their personal data, typically with a strict response deadline of 30 days. For large enterprises with data scattered across cloud, SaaS, and on-prem environments, even a single request can trigger a frantic search across multiple systems, manual reviews, and legal oversight - quickly turning DSAR compliance into a race against the clock, with reputation and regulatory risk on the line.

What Is a Data Subject Access Request (DSAR)?

A Data Subject Access Request (DSAR) is a legal right granted under privacy regulations such as GDPR and CPRA that allows individuals to request access to the personal data an organization holds about them. In many cases, individuals can also request information about how that data is used, shared, or deleted.

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Organizations are typically required to respond to DSARs within a strict timeframe, often 30 days, and must provide a complete and accurate view of the individual’s personal data. This includes data stored in databases, files, logs, SaaS platforms, and other systems across the organization.

Why DSAR Requests Are Difficult to Manage at Scale

DSARs are relatively manageable for small organizations with limited systems. At enterprise scale, however, they become significantly more complex. Personal data is no longer centralized. It is distributed across cloud platforms, SaaS applications, data lakes, file systems, and legacy infrastructure. Privacy teams must coordinate with IT, security, legal, and data owners to locate, review, and validate data before responding. As DSAR volumes increase, manual processes quickly break down, increasing the risk of delays, incomplete responses, and regulatory exposure.

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Key Challenges in Responding to DSARs

Data Discovery & Inventory

‍For large organizations, pinpointing where personal data resides across a diverse ecosystem of information systems, including databases, SaaS applications, data lakes, and legacy environments, is a complex challenge. The presence of fragmented IT infrastructure and third-party platforms often leads to limited visibility, which not only slows down the DSAR response process but also increases the likelihood of missing or overlooking critical personal data.

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Linking Identities Across Systems‍

A single individual may appear in multiple systems under different identifiers, especially if systems have been acquired or integrated over time. Accurately correlating these identities to compile a complete DSAR response requires sophisticated identity resolution and often manual effort.

Unstructured Data Handling

‍Unlike structured databases, where data is organized into labeled fields and can be efficiently queried, unstructured data (like PDFs, documents, and logs) is free-form and lacks consistent formatting. This makes it much harder to search, classify, or extract relevant personal information.
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Response Timeliness

‍Regulatory deadlines force organizations to respond quickly, even when data must be gathered from multiple sources and reviewed by legal teams. Manual processes can lead to delays, risking non-compliance and fines.

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Volume & Scalability

‍While most organizations can handle an occasional DSAR manually, spikes in request volume - driven by events like regulatory campaigns or publicized incidents - can overwhelm privacy and legal teams. Without scalable automation, organizations face mounting operational costs, missed deadlines, and an increased risk of inconsistent or incomplete responses.

The Role of Data Security Platforms in DSAR Automation

Sentra is a modern data security platform dedicated to helping organizations gain complete visibility and control over their sensitive data. By continuously scanning and classifying data across all environments (including cloud, SaaS, and on-premises systems) Sentra maintains an always up-to-date data map, giving organizations a clear understanding of where sensitive data resides, how it flows, and who has access to it. This data map forms the foundation for efficient DSAR automation, enabling Sentra’s DSAR module to search for user identifiers only in locations where relevant data actually exists - ensuring high accuracy, completeness, and fast response times.

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Another key factor in managing DSAR requests is ensuring that sensitive customer PII doesn’t end up in unauthorized or unintended environments. When data is copied between systems or environments, it’s essential to apply tokenization or masking to prevent unintentional sprawl of PII. Sentra helps identify misplaced or duplicated sensitive data and alerts when it isn’t properly protected. This allows organizations to focus DSAR processing within authorized operational environments, significantly reducing both risk and response time.

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Smart Search of Individual Data

To initiate the generation of a Data Subject Access Request (DSAR) report, users can submit one or more unique identifiers—such as email addresses, Social Security numbers, usernames, or other personal identifiers—corresponding to the individual in question. Sentra then performs a targeted scan across the organization’s data ecosystem, focusing on data stores known to contain personally identifiable information (PII). This includes production databases, data lakes, cloud storage services, file servers, and both structured and unstructured data sources.

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Leveraging its advanced classification and correlation capabilities, Sentra identifies all relevant records associated with the provided identifiers. Once the scan is complete, it compiles a comprehensive DSAR report that consolidates all discovered personal data linked to the data subject that can be downloaded as a PDF for manual review or securely retrieved via Sentra’s API.

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Establishing a DSAR Processing Pipeline
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Large organizations that receive a high volume of DSAR (Data Subject Access Request) submissions typically implement a robust, end-to-end DSAR processing pipeline. This pipeline is often initiated through a self-service privacy portal, allowing individuals to easily submit requests for access or deletion of their personal data. Once a request is received, an automated or semi-automated workflow is triggered to handle the request efficiently and in compliance with regulatory timelines.

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1. Requester Identity Verification: Confirm the identity of the data subject to prevent unauthorized access (e.g., via email confirmation or secure login).
   
   
2. Mapping Identifiers: Collect and map all known identifiers for the individual across systems (e.g., email, user ID, customer number).
   
   
3. Environment-Wide Data Discovery (via Sentra): Use Sentra to search all relevant environments — cloud, SaaS, on-prem — for personal data tied to the individual. By using Sentra’s automated discovery and classification, Sentra can automatically identify where to search for.
   
   
4. DSAR Report Generation (via Sentra): Compile a detailed report listing all personal data found and where it resides.
   
   
5. Data Deletion & Verification: Remove or anonymize personal data as required, then rerun a search to verify deletion is complete.
   
   
6. Final Response to Requester: Send a confirmation to the requester, outlining the actions taken and closing the request.

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Sentra plays a key role in the DSAR pipeline by exposing a powerful API that enables automated, organization-wide searches for personal data. The search results can be programmatically used to trigger downstream actions like data deletion. After removal, the API can initiate a follow-up scan to verify that all data has been successfully deleted.

Benefits of DSAR Automation 

With privacy regulations constantly growing, and DSAR volumes continuing to rise, building an automated, scalable pipeline is no longer a luxury - it’s a necessity.

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• Automated and Cost-Efficient: Replaces costly, error-prone manual processes with a streamlined, automated approach.
  
• High-Speed, High-Accuracy: Sentra leverages its knowledge of where PII resides to perform targeted searches across all environments and data types, delivering comprehensive reports in hours—not days.
  
• Seamless Integration: A powerful API allows integration with workflow systems, enabling a fully automated, end-to-end DSAR experience for end users.

By using Sentra to intelligently locate PII across all environments, organizations can eliminate manual bottlenecks and accelerate response times. Sentra’s powerful API and deep data awareness make it possible to automate every step of the DSAR journey - from discovery to deletion - enabling privacy teams to operate at scale, reduce costs, and maintain compliance with confidence. 

Turning DSAR Compliance into a Scalable Advantage with Automation

As privacy expectations grow and regulatory pressure intensifies, DSARs are no longer just a compliance checkbox, they are a reflection of how seriously an organization treats user trust. Manual, reactive processes simply cannot keep up with the scale and complexity of modern data environments, especially as personal data continues to spread across cloud, SaaS, and on-prem systems.

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By automating DSAR workflows with a data-centric security platform like Sentra, organizations can respond faster, reduce compliance risk, and lower operational costs - all while freeing privacy and legal teams to focus on higher-value initiatives. In this way, DSAR compliance becomes not just a regulatory obligation, but a measure of operational maturity and a scalable advantage in building long-term trust.

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Learn how to build automated data security policies that reduce data exposure, meet GDPR, PCI DSS, and HIPAA requirements, and scale data governance across cloud, SaaS, and AI-driven environments as organizations move into 2026.

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As 2025 comes to a close, one reality is clear: automated data security and governance programs are a must-have to truly leverage data and AI. Sensitive data now moves faster than human review can keep up with. It flows across multi-cloud storage, SaaS platforms, collaboration tools, logging pipelines, backups, and increasingly, AI and analytics workflows that continuously replicate data into new locations. For security and compliance teams heading into 2026, periodic audits and static policies are no longer sufficient. Regulators, customers, and boards now expect continuous visibility and enforcement.

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This is why automated data security policies have become a foundational control, not a “nice to have.”

In this blog, we focus on how data security policies are actually used at the end of 2025, and how to design them so they remain effective in 2026.

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You’ll learn:

• The most important compliance and risk-driven policy use cases
• How organizations operationalize data security policies at scale
• Practical examples aligned with GDPR, PCI DSS, HIPAA, and internal governance
  

Why Automated Data Security Policies Matter Heading into 2026

The direction of regulatory enforcement and threat activity is consistent:

• Continuous compliance is now expected, not implied
• Overexposed data is increasingly used for extortion, not just theft
• Organizations must prove they know where sensitive data lives and who can access it
  

Recent enforcement actions have shown that organizations can face penalties even without a breach, simply for storing regulated data in unapproved locations or failing to enforce access controls consistently.

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Automated data security policies address this gap by continuously evaluating:

• Data sensitivity
• Access scope
• Storage location and residency
• surfacing violations in near real time.

Three Data Security Policy Use Cases That Deliver Immediate Value

As organizations prepare for 2026, most start with policies that reduce data  exposure quickly.

1. Limiting Data Exposure and Ransomware Impact

Misconfigured access and excessive sharing remain the most common causes of data exposure. In cloud and SaaS environments, these issues often emerge gradually, and go unnoticed without automation.

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High-impact policies include:

• Sensitive data shared with external users: Detect files containing credentials, PII, or financial data that are accessible to outside collaborators.
  
• Overly broad internal access to sensitive data: Identify data shared with “Anyone in the organization,” significantly increasing exposure during account compromise.
  

These policies reduce blast radius and help prevent data from becoming leverage in extortion-based attacks.

2. Enforcing Secure Data Storage and Handling (PCI DSS, HIPAA, SOC 2)

Compliance violations in 2025 rarely result from intentional misuse. They happen because sensitive data quietly appears in the wrong systems.

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Common policy findings include:

• Payment card data in application logs or monitoring tools: A persistent PCI DSS issue, especially in modern microservice environments.
  
• Employee or patient records stored in collaboration platforms: PII and PHI often end up in user-managed drives without appropriate safeguards.
  

Automated policies continuously detect these conditions and support fast remediation, reducing audit findings and operational risk.

3. Maintaining Data Residency and Sovereignty Compliance

As global data protection enforcement intensifies, data residency violations remain one of the most common and costly compliance failures.

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Automated policies help identify:

• EU personal data stored outside approved EU regions: A direct GDPR violation that is common in multi-cloud and SaaS environments.
  
• Cross-region replicas and backups containing regulated data: Secondary storage locations frequently fall outside compliance controls.
  
  

These policies enable organizations to demonstrate ongoing compliance, not just point-in-time alignment.

What Modern Data Security Policies Must Do (2026-Ready)

As teams move into 2026, effective data security policies share three traits:

1. They are data-aware: Policies are based on data sensitivity - not just resource labels or storage locations.
   
2. They operate continuously: Policies evaluate changes as data is created, moved, shared, or copied into new systems.
   
3. They drive action: Every violation maps to a remediation path: restrict access, move data, or delete it.
   

This is what allows security teams to scale governance without slowing the business.

Conclusion: From Static Rules to Continuous Data Governance

Heading into 2026, automated data security policies are no longer just compliance tooling, they are a core layer of modern security architecture.

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They allow organizations to:

• Reduce exposure and ransomware risk
• Enforce regulatory requirements continuously
• Govern sensitive data across cloud, SaaS, and AI workflows

Most importantly, they replace reactive audits with real-time data governance.

Organizations that invest in automated, data-aware security policies today will enter 2026 better prepared for regulatory scrutiny, evolving threats, and the continued growth of their data footprint.

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Data Security Posture Management (DSPM) has quickly become part of mainstream security, gaining ground on older solutions and newer categories like XDR and SSE. Beneath the hype, most security leaders share the same frustration: too many products promise results but simply can't deliver in the messy, large-scale settings that enterprises actually have. The DSPM market is expected to jump from $1.86B in 2024 to $22.5B by 2033, giving buyers more choice - and greater pressure - to demand what really sets a solution apart for the coming years.

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Instead of letting vendors dictate the RFP, what if CISOs led the process themselves? Fast-forward to 2026 and the checklist a CISO uses to evaluate DSPM solutions barely resembles the checklists of the past. Here are the 12 criteria everyone should insist on - criteria most vendors would rather you ignore, but industry leaders like Sentra are happy to highlight.

Why Legacy DSPM Evaluation Fails Modern CISOs

Traditional DSPM/DCAP evaluations were all about ticking off feature boxes: Can it scan S3 buckets? Show file types? But most CISO I meet point to poor data visibility as their biggest vulnerability. It's already obvious that today’s fragmented, agent-heavy tools aren’t cutting it.

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So, what’s changed for 2026? Massive data volumes, new unstructured formats like chat logs or AI training sets, and rapid cloud adoption mean security leaders now need a different class of protection.

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The right platform:

• Works without agents, everywhere you operate
• Focuses on bringing real, risk-based context - not just adding more alerts
• Automates compliance and fixes identity/data governance gaps
• Manages both structured and unstructured data across the whole organization

Old evaluation checklists don’t come close. It’s time to update yours.

The 13 DSPM Buying Criteria Vendors Hope You Don’t Ask

Here’s what should be at the heart of every modern assessment, especially for 2026:

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1. Is the platform truly agentless, everywhere? Agent-based designs slow you down and block coverage. The best solutions set up in minutes, with absolutely no agents - across SaaS, IaaS, or on-premises and will always discover any unknown and shadow data

2. Does it operate fully in-environment? Your data needs to stay in your cloud or region - not copied elsewhere for analysis. In-environment processing guards privacy, simplifies compliance, and matches global regulations (Cloud Security Alliance).

3. Can it accurately classify unstructured data (>98% accuracy)? Most tools stumble outside of databases. Insist on AI-powered classification that understands language, context, and sensitivity. This covers everything from PDF files to Zoom recordings to LLM training data.

4. How does it handle petabyte-scale scanning and will it  break the bank? Legacy options get expensive as data grows. You need tools that can scan quickly and stay cost-effective across multi-cloud and hybrid environments at massive scale.

5. Does it unify data and identity governance? Very few platforms support both human and machine identities - especially for service accounts or access across clouds. Only end-to-end coverage breaks down barriers between IT, business, and security.

6. Can it surface business-contextualized risk insights? You need more than technical vulnerability. Leading platforms map sensitive data by its business importance and risk, making it easier to prioritize and take action.

7. Is deployment frictionless and multi-cloud native? DSPM should work natively in AWS, Azure, GCP, and SaaS, no complicated integrations required. Insist on fast, simple onboarding.

8. Does it offer full remediation workflow automation? It’s not enough to raise the alarm. You want exposures fixed automatically, at scale, without manual effort.
   
   
9. Does this fit within my Data Security Ecosystem? Choose only platforms that integrate and enrich your current data governance stack so every tool operates from the same source of truth without adding operational overhead. 

10. Are compliance and security controls bridged in a unified dashboard? No more switching between tools. Choose platforms where compliance and risk data are combined into a single view for GRC and SecOps.

11. Does it support business-driven data discovery (e.g., by project, region, or owner)? You need dynamic views tied to business needs, helping cloud initiatives move faster without adding risk, so security can become a business enabler.

12. What’s the track record on customer outcomes at scale? Actual results in complex, high-volume settings matter more than demo promises. Look for real stories from large organizations.
13. ‍How is pricing structured for future growth? Beware of pricing that seems low until your data doubles. Look for clear, usage-based models so expansion won’t bring hidden costs.

Agentless, In-Environment Power: Why It’s the New Gold Standard

Agentless, in-environment architecture removes hassles with endpoint installs, connectors, and worries about where your data goes. Gartner has highlighted that this approach reduces regulatory headaches and enables fast onboarding. As organizations keep adding new cloud and hybrid systems, only these platforms can truly scale for global teams and strict requirements.

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Sentra’s platform keeps all processing inside your environment. There’s no need to export your data; offering peace of mind for privacy, sovereignty, and speed. With regulations increasing everywhere, this approach isn’t just helpful; it’s essential.

Classification Accuracy and Petabyte-Scale Efficiency: The Must-Haves for 2026

Unstructured data is growing fast, and workloads are now more diverse than ever. The difference between basic scanning and real, AI-driven classification is often the difference between protecting your company or ending up on the breach list. Leading platforms, including Sentra, deliver over 95% classification accuracy by using large language models and in-house methods across both structured and unstructured data.

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Why is speed and scale so important? Old-school solutions were built with smaller data volumes in mind. Today, DSPM platforms must quickly and affordably identify and secure data in vast environments. Sentra’s scanning is both fast and affordable, keeping up as your data grows. To learn more about these challenges read: Reducing Cloud Data Attack Risk.

Don’t Settle: Redefining Best-in-Class DSPM Buying Criteria for 2026

Many vendors are still only comfortable offering the basics, but the demands facing CISOs today are anything but basic. Combining identity and data governance, multi-cloud support that works out of the box, and risk insights mapped to real business needs - these are the essential elements for protecting today’s and tomorrow’s data. If a solution doesn’t check all 12 boxes, you’re already limiting your security program before you start.

Need a side-by-side comparison for your next decision?  Request a personalized demo to see exactly how Sentra meets every requirement.

Conclusion

With AI further accelerating data growth, security teams can’t afford to settle for legacy features or generic checklists. By insisting on meaningful criteria - true agentless design, in-environment processing, precise AI-driven classification, scalable affordability, and business-first integration - CISOs set a higher standard for both their own organizations and the wider industry.

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Sentra is ready to help you raise the bar. Contact us for a data risk assessment, or to discuss how to ensure your next buying decision leads to better protection, less risk, and a stronger position for the future.

Continue the Conversation

If you want to go deeper into how CISOs are rethinking data security, I explore these topics regularly on Guardians of the Data, a podcast focused on real-world data protection challenges, evolving DSPM strategies, and candid conversations with security leaders.

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Watch or listen to Guardians of the Data for practical insights on securing data in an AI-driven, multi-cloud world.

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We knew we were on a hot streak, and now it’s official.

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Sentra has been named one of CRN’s 10 Hottest Cybersecurity Startups of 2025. This recognition is a direct reflection of our commitment to redefining data security for the cloud and AI era, and of the growing trust forward-thinking enterprises are placing in our unique approach.

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This milestone is more than just an award. It shows our relentless drive to protect modern data systems and gives us a chance to thank our customers, partners, and the Sentra team whose creativity and determination keep pushing us ahead.

The Market Forces Fueling Sentra’s Momentum

Cybersecurity is undergoing major changes. With 94% of organizations worldwide now relying on cloud technologies, the rapid growth of cloud-based data and the rise of AI agents have made security both more urgent and more complicated. These shifts are creating demands for platforms that combine unified data security posture management (DSPM) with fast data detection and response (DDR).

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Industry data highlights this trend: over 73% of enterprise security operations centers are now using AI for real-time threat detection, leading to a 41% drop in breach containment time. The global cybersecurity market is growing rapidly, estimated to reach $227.6 billion in 2025, fueled by the need to break down barriers between data discovery, classification, and incident response 2025 cybersecurity market insights. In 2025, organizations will spend about 10% more on cyber defenses, which will only increase the demand for new solutions.

Why Recognition by CRN Matters and What It Means

Landing a place on CRN’s 10 Hottest Cybersecurity Startups of 2025 is more than publicity for Sentra. It signals we truly meet the moment. Our rise isn’t just about new features; it’s about helping security teams tackle the growing risks posed by AI and cloud data head-on. This recognition follows our mention as a CRN 2024 Stellar Startup, a sign of steady innovation and mounting interest from analysts and enterprises alike.

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Being on CRN’s list means customers, partners, and investors value Sentra’s straightforward, agentless data protection that helps organizations work faster and with more certainty.

Innovation Where It Matters: Sentra’s Edge in Data and AI Security

Sentra stands out for its practical approach to solving urgent security problems, including:

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• Agentless, multi-cloud coverage: Sentra identifies and classifies sensitive data and AI agents across cloud, SaaS, and on-premises environments without any agents or hidden gaps.
• Integrated DSPM + DDR: We go further than monitoring posture by automatically investigating incidents and responding, so security teams can act quickly on why DSPM+DDR matters.
• AI-driven advancements: Features like domain-specific AI Classifiers for Unstructure advanced AI classification leveraging SLMs, Data Security for AI Agents and Microsoft M365 Copilot help customers stay in control as they adopt new technologies Sentra’s AI-powered innovation.

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With new attack surfaces popping up all the time, from prompt injection to autonomous agent drift, Sentra’s architecture is built to handle the world of AI.

A Platform Approach That Outpaces the Competition

There are plenty of startups aiming to tackle AI, cloud, and data security challenges. Companies like 7AI, Reco, Exaforce, and Noma Security have been in the news for their funding rounds and targeted solutions. Still, very few offer the kind of unified coverage that sets Sentra apart.

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Most competitors stick to either monitoring SaaS agents or reducing SOC alerts. Sentra does more by providing both agentless multi-cloud DSPM and built-in DDR. This gives organizations visibility, context, and the power to act in one platform. With features like Data Security for AI Agents, Sentra helps enterprises go beyond managing alerts by automating meaningful steps to defend sensitive data everywhere.

Thanks to Our Community and What’s Next

This honor belongs first and foremost to our community: customers breaking new ground in data security, partners building solutions alongside us, and a team with a clear goal to lead the industry.

If you haven’t tried Sentra yet, now’s a great time to see what we can do for your cloud and AI data security program. Find out why we’re at the forefront: schedule a personalized demo or read CRN’s full 2025 list for more insight.

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Conclusion

Being named one of CRN’s hottest cybersecurity startups isn’t just a milestone. It pushes us forward toward our vision - data security that truly enables innovation. The market is changing fast, but Sentra’s focus on meaningful security results hasn't wavered.

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Thank you to our customers, partners, investors, and team for your ongoing trust and teamwork. As AI and cloud technology shape the future, Sentra is ready to help organizations move confidently, securely, and quickly.

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Generative AI isn’t just transforming products and processes - it’s expanding the entire enterprise risk surface. As C-suite executives and security leaders rush to unlock GenAI’s competitive advantages, a hard truth is clear: effective AI governance depends on solid, end-to-end data governance.

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Sensitive data is increasingly used for model training and autonomous agents. If organizations fail to discover, classify, and secure these resources early, they risk privacy breaches, regulatory violations, and reputational damage. To make GenAI safe, compliant, and trustworthy from the start, data governance for generative AI needs to be a top boardroom priority.

Why Data Governance is the Cornerstone of GenAI Trustworthiness and Safety

The opportunities and risks of generative AI depend not only on algorithms, but also on the quality, security, and history of the underlying data. AWS reports that 39% of Chief Data Officers see data cleaning, integration, and storage as the main barriers to GenAI adoption, and 49% of enterprises make data quality improvement a core focus for successful AI projects (AWS Enterprise Strategy - Data Governance). Without strong data governance, sensitive information can end up in training sets, leading to unintentional leaks or model behaviors that break privacy and compliance.

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Regulatory requirements, such as the Generative AI Copyright Disclosure Act, are evolving fast, raising the pressure to document data lineage and make sure unauthorized or non-compliant datasets stay out. In the world of GenAI, governance goes far beyond compliance checklists. It’s essential for building AI that is safe, auditable, and trusted by both regulators and customers.

New Attack Surfaces: Risks From Unsecured Data and Shadow AI Agents

GenAI adoption increases risk. Today, 79% of organizations have already piloted or deployed agentic AI, with many using LLM-powered agents to automate key workflows (Wikipedia - Agentic AI). But if these agents, sometimes functioning as "shadow AI" outside official oversight, access sensitive or unclassified data, the fallout can be severe.

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In 2024, over 30% of AI data breaches involve insider threats or accidental disclosure, according to Quinnox Data Governance for AI. Autonomous agents can mistakenly reveal trade secrets, financial records, or customer data, damaging brand trust. The risk multiplies rapidly if sensitive data isn’t properly governed before flowing into GenAI tools. To stop these new threats, organizations need up-to-the-minute insight and control over both data and the agents using it.

Frameworks and Best Practices for Data Governance in GenAI

Leading organizations now follow data governance frameworks that match changing regulations and GenAI's technical complexity. Standards like NIST AI Risk Management Framework (AI RMF) and ISO/IEC 42001:2023 are setting the benchmarks for building auditable, resilient AI programs (Data and AI Governance - Frameworks & Best Practices).

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Some of the most effective practices:

• Managing metadata and tracking full data lineage
• Using data access policies based on role and context
• Automating compliance with new AI laws
• Monitoring data integrity and checking for bias

A strong data governance program for generative AI focuses on ongoing data discovery, classification, and policy enforcement - before data or agents meet any AI models. This approach helps lower risk and gives GenAI efforts a solid base of trust.

Sentra’s Approach: Proactive Pre-Integration Discovery and Continuous Enforcement

Many tools only secure data after it’s already being used with GenAI applications. This reactive strategy leaves openings for risk. Sentra takes a different path, letting organizations discover, classify, and protect sensitive data sources before they interact with language models or agentic AI.

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By using agentless, API-based discovery and classification across multi-cloud and SaaS environments, Sentra delivers immediate visibility and context-aware risk scoring for all enterprise data assets. With automated policies, businesses can mask, encrypt, or restrict data access depending on sensitivity, business requirements, or audit needs. Live Continuous monitoring tracks which AI agents are accessing data, making granular controls and fast intervention possible. These processes help stop shadow AI, keep unauthorized data out of LLM training, and maintain compliance as rules and business needs shift.

Guardrails for Responsible AI Growth Across the Enterprise

The future of GenAI depends on how well businesses can innovate while keeping security and compliance intact. As AI regulations become stricter and adoption speeds up, Sentra’s ability to provide ongoing, automated discovery and enforcement at scale is critical. Further reading: AI Automation & Data Security: What You Need To Know.

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With Sentra, organizations can:

• Stop unapproved or unchecked data from being used in model training
• Identify shadow AI agents or risky automated actions as they happen
• Support audits with complete data classification
• Meet NIST, ISO, and new global standards with ease

Sentra gives CISOs, CDOs, and executives a proactive, scalable way to adopt GenAI safely, protecting the business before any model training even begins.

AI Governance Starts with Data Governance

AI governance for generative AI starts, and is won or lost, at the data layer. If organizations don’t find, classify, and secure sensitive data first, every other security measure remains reactive and ineffective. As generative AI, agent automation, and regulatory demands rise, a unified data governance strategy isn’t just good practice, it’s an urgent priority. Sentra gives security and business teams real control, making sure GenAI is secure, compliant, and trusted.

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By 2026, American data privacy will look very different as a wave of new state laws redefines what it means to protect sensitive information. Organizations face a regulatory maze: more than 20 states will soon require not only “reasonable security” but also Data Protection Impact Assessments (DPIAs), explicit limits on data collection, and, in some cases, detailed data inventories. These requirements are quickly becoming standard, and ignoring them simply isn’t an option. The risk of penalties and enforcement actions is climbing fast.

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But through all these changes, one major question remains: How can any organization comply if it doesn’t even know where its most sensitive data is? Data Security Posture Management (DSPM) has become the solution, making data visibility and automation central for meeting ongoing compliance needs.

Mapping the New Wave of State Privacy Mandates

Several state privacy laws going into effect in 2025 and 2026 are raising the stakes for compliance. Kentucky, Indiana, and Rhode Island’s new laws, effective January 1, 2026, require both security measures and DPIAs for handling high-risk or sensitive data. Minnesota’s law stands out even more: it moves past earlier vague “reasonable” security language and mandates comprehensive data inventories.

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Other key states include Minnesota, which explicitly requires data inventories, Maryland with strict data minimization rules, and Tennessee, which gives organizations an affirmative defense if they’ve adopted a NIST-aligned privacy program. These requirements mean organizations now need to track what data they collect, know exactly where it’s stored, and show evidence of compliance when asked. If your organization operates in more than one state, keeping up with this web of laws will soon become impossible without dedicated solutions (US consumer privacy laws 2025 update).

Why Data Visibility is Now Foundational to Compliance

To meet DPIA, minimization, and security safeguard rules, you need full visibility into where sensitive or regulated data lives - and how it moves across your environment. Recent privacy laws are moving closer to GDPR-like standards, with DPIAs required not only for biometric data but also for broad categories like targeted advertising and profiling. Minnesota leads with its clear requirement for full data inventories, setting the standard that you can’t prove compliance unless you understand your data (US cybersecurity and data privacy review and outlook 2025).

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This shift puts DSPM front and center: you now need ongoing discovery and classification of your entire sensitive data footprint. Without a strong data foundation, organizations will find it hard to complete DPIAs, handle audits, or defend themselves in investigations.

Automation: The Only Viable Path for Assessment and Audit Readiness

State privacy rules are getting more complicated, and many enforcement authorities are shortening or removing 'right-to-cure' periods. That means manual compliance simply won’t keep up. Automation is now the only way to manage compliance as regulations tighten (5 trends to watch: 2025 US data privacy & cybersecurity).

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With DSPM and automation, organizations get ongoing discovery, real-time data classification, and instant evidence collection - all required for fast DPIAs and responsive audits. For companies facing regulators or preparing for multi-state oversight, this means you already have the proof and documentation you need. Relying on spreadsheets or one-time assessments at this point only increases your risk.

Sentra: Your Strategic Bridge to Privacy Law Compliance

Sentra’s DSPM platform is built to tackle these expanding privacy law requirements. The agentless platform covers AWS, Azure, GCP, SaaS, and hybrid environments, removing both visibility gaps and the hassle found in older solutions (Sentra: DSPM for compliance use cases).

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With continuous, automated discovery and data classification, you always know exactly where your sensitive data is, how it moves, and how it’s being protected. Sentra’s integrated Data Detection & Response (DDR) catches and fixes risks or policy violations early, closing gaps before regulators - or attackers - can take advantage (Sensitive data exposure insight). Combined with clear reporting and on-demand audit documentation, Sentra helps you meet new state privacy laws and stay audit-ready, even as your business or data needs change.

Conclusion

The arrival of new state privacy laws in 2025 and 2026 is changing how organizations must handle sensitive data. Security safeguards, DPIAs, minimization, and full inventories are now required - not just nice-to-have.

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DSPM is now a compliance must-have. Without complete data visibility and automation, following the web of state rules isn’t difficult - it’s impossible. Sentra’s agentless, multi-cloud platform keeps your organization continuously informed, giving compliance, security, and privacy teams the control they need to keep up with new regulations.

Want to see how your organization stacks up for 2026 laws? Book a DSPM Compliance Readiness Assessment or check out Sentra’s automated DPIA tools today.

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Cloud adoption and the explosion of data have boosted business agility, but they’ve also created new headaches for security teams. As companies move sensitive information into multi-cloud and hybrid environments, old security models start to break down. Shuffling data for scanning and classification adds risk, piles on regulatory complexity, and drives up operational costs.

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Zero Data Movement (ZDM) offers a new architectural approach, reshaping how advanced Data Security Posture Management (DSPM) platforms provide visibility, protection, and compliance. This post breaks down what makes ZDM unique, why it matters for security-focused enterprises, and how Sentra provides an innovative agentless and scalable design that is genuinely a zero data movement DSPM .

Defining Zero Data Movement Architecture

Zero Data Movement (ZDM) sets a new standard in data security. The premise is straightforward: sensitive data should stay in its original environment for security analysis, monitoring, and enforcement. Older models require copying, exporting, or centralizing data to scan it, while ZDM ensures that all security actions happen directly where data resides.

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ZDM removes egress risk -shrinking the attack surface and reducing regulatory issues. For organizations juggling large cloud deployments and tight data residency rules, ZDM isn’t just an improvement - it's essential. Groups like the Cloud Security Alliance and new privacy regulations are moving the industry toward designs that build in privacy and non-stop protection.

Risks of Data Movement: Compliance, Cost, and Egress Exposure

Every time data is copied, exported, or streamed out of its native environment, new risks arise. Data movement creates challenges such as:

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• Egress risk: Data at rest or in transit outside its original environment  increases risk of breach, especially as those environments may be less secure.
• Compliance and regulatory exposure: Moving data across borders or different clouds can break geo-fencing and privacy controls, leading to potential violations and steep fines.
• Loss of context and control: Scattered data makes it harder to monitor everything, leaving gaps in visibility.
• Rising total cost of ownership (TCO): Scanning and classification can incur heavy cloud compute costs - so efficiency matters.  Exporting or storing data, especially shadow data, drives up storage, egress, and compliance costs as well.

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As more businesses rely on data, moving it unnecessarily only increases the risk - especially with fast-changing cloud regulations.

Legacy and Competitor Gaps: Why Data Movement Still Happens

Not every security vendor practices true zero data movement, and the differences are notable. Products from Cyera, Securiti, or older platforms still require temporary data exporting or duplication for analysis. This might offer a quick setup, but it exposes users to egress risks, insider threats, and compliance gaps - problems that are worse in regulated fields.

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Competitors like Cyera often rely on shortcuts that fall short of ZDM’s requirements. Securiti and similar providers depend on connectors, API snapshots, or central data lakes, each adding potential risks and spreading data further than necessary. With ZDM, security operations like monitoring and classification happen entirely locally, removing the need to trust external storage or aggregation. For more detail on how data movement drives up risk.

The Business Value of Zero Data Movement DSPM

Zero data movement DSPM changes the equation for businesses:

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• Designed for compliance: Data remains within controlled environments, shrinking audit requirements and reducing breach likelihood.
• Lower TCO and better efficiency: Eliminates hidden expenses from extra storage, duplicate assets, and exporting to external platforms.
• Regulatory clarity and privacy: Supports data sovereignty, cross-border rules, and new zero trust frameworks with an egress-free approach.

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Sentra’s agentless, cloud-native DSPM provides these benefits by ensuring sensitive data is never moved or copied. And Sentra delivers these benefits at scale - across multi-petabyte enterprise environments - without the performance and cost tradeoffs others suffer from. Real scenarios show the results: financial firms keep audit trails without data ever leaving allowed regions. Healthcare providers safeguard PHI at its source. Global SaaS companies secure customer data at scale, cost-effectively while meeting regional rules.

Future-Proofing Data Security: ZDM as the New Standard

With data volumes expected to hit 181 zettabytes in 2025, older protection methods that rely on moving data can’t keep up. Zero data movement architecture meets today's security demands and supports zero trust, metadata-driven access, and privacy-first strategies for the future.

Companies wanting to avoid dead ends should pick solutions that offer unified discovery, classification and policy enforcement without egress risk. Sentra’s ZDM architecture makes this possible, allowing organizations to analyze and protect information where it lives, at cloud speed and scale.

Conclusion

Zero Data Movement is more than a technical detail - it's a new architectural standard for any organization serious about risk control, compliance, and efficiency. As data grows and regulations become stricter, the old habits of moving, copying, or centralizing sensitive data will no longer suffice.

Sentra stands out by delivering a zero data movement DSPMplatform that's agentless, real-time, and truly multicloud. For security leaders determined to cut egress risk, lower compliance spending, and get ahead in privacy, ZDM is the clear path forward.

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As organizations scramble to secure their sprawling cloud environments and deploy AI, many are facing a stark realization: handling petabyte-scale data is now a basic security requirement. With sensitive information multiplying across multiple clouds, SaaS, and AI-driven platforms, security leaders can't treat true data security at scale as a simple add-on or upgrade.

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At the same time, speeding up digital transformation means higher and less visible operational costs for handling this data surge. Older Data Security Posture Management (DSPM) tools, especially those boasting broad, indiscriminate scans as evidence of their scale, are saddling organizations with rising cloud bills, slowdowns, and dangerous gaps in visibility. The costs of securing petabyte-scale data are now economic and technical, demanding efficiency instead of just scale. Sentra solves this with a highly-efficient cloud-native design, delivering 10x lower cloud compute costs.

Why Petabyte Scale is a Security Requirement

Data environments have exploded in both size and complexity. For Fortune 500 companies, fast-growing SaaS providers, and global organizations, data exists across public and hybrid clouds, business units, regions, and a stream of new applications.

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Regulations such as GDPR, HIPAA, and rules from the SEC now demand current data inventories and continuous proof of risk management. In this environment, defending data at the petabyte level is now essential. Failing to classify and monitor this data efficiently means risking compliance and losing business trust. Security teams are feeling the strain. I meet security teams everyday and too many of them still struggle with data visibility and are already seeing the cracks forming in their current toolset as data scales.

The Hidden Cost of Inefficient DSPM: API Calls and Egress Bills

How DSPM tools perform scanning and discovery drives the real costs of securing petabyte-scale data. Some vendors highlight their capacity to scan multiple petabytes daily. But here's the reality: scanning everything, record by record, relying on huge numbers of API calls, becomes very expensive as your data estate grows.

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Every API call can rack up costs, and all the resulting data egress and compute add up too. Large organizations might spend tens of thousands of dollars each month just to track what’s in their cloud. Even worse, older "full scan" DSPM strategies jam up operations with throttling, delays, and a flood of alerts that bury real risk. These legacy approaches simply don’t scale, and organizations relying on them end up paying more while knowing less.

Cyera’s "Petabyte Scale" Claims: At What Cloud Cost?

Cyera promotes its tool as an AI-native, agentless DSPM that can scan as much as 2 petabytes daily . While that’s an impressive technical achievement, the strategy of scanning everything leads directly to massive cloud infrastructure costs: frequent API hits, heavy egress, and big bills from AWS, Azure, and GCP.

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At scale, these charges don’t just appear on invoices, they can actually stop adoption and limit security’s effectiveness. Cloud operations teams face API throttling, slow results, and a surge in remediation tickets as risks go unfiltered. In these fast-paced environments, recognizing the difference between a real threat and harmless data comes down to speed. The Bedrock Security blog points out how inefficient setups buckle under this weight, leaving teams stuck with lagging visibility and more operational headaches.

Sentra’s 10x Efficiency: Optimized Scanning for Real-World Scale

Sentra takes another route to manage the costs of securing petabyte-scale data. By combining agentless discovery with scanning guided by context and metadata, Sentra uses pattern recognition and an AI-driven clustering algorithm designed to detect machine-generated content—such as log files, invoices, and similar data types. By intelligently sampling data within each cluster, Sentra delivers efficient scanning while reducing scanning costs.

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This approach enables data scanning to be prioritized based on risk and business value, rather than wasting time and money scanning the same data over and over again, skipping unnecessary API calls, lowering egress, and keeping cloud bills in check.

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Large organizations gain a 10x efficiency edge: quicker classification of data, instant visibility into actual threats, lower operational expenses, and less demand on the network. By focusing attention only where it matters, Sentra matches data security posture management to the demands of current cloud growth and regulatory requirements.

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This makes it possible for organizations to hit regulatory and audit targets without watching expenses spiral or opening up security gaps.Sentra offers multiple sampling levels, Quick (default), Moderate, Thorough, and Full, allowing customers to tailor their scanning strategy to balance cost and accuracy. For example, a highly regulated environment can be configured for a full scan, while less-regulated environments can use more efficient sampling. Petabyte-scale security gives the user complete control of their data enterprise and turns into something operationally and financially sustainable, rather than a technical milestone with a hidden cost. 

Efficiency is Non-Negotiable

Fortune 500 companies and digital-first organizations can’t treat efficiency as optional. Inefficient DSPM tools pile on costs, drain resources, and let vulnerabilities slip through, turning their security posture into a liability once scale becomes a factor. Sentra’s platform shows that efficiency is security: with targeted scanning, real context, and unified detection and response, organizations gain clarity and compliance while holding down expenses.

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Don’t let your data protection approach crumble under petabyte-scale pressure. See what Sentra can do, reduce costs, and keep essential data secure - before you end up responding to breaches or audit failures.

Conclusion

Securing data at the petabyte level isn't some future aspiration - it's the standard for enterprises right now. Treating it as a secondary feature isn’t just shortsighted; it puts your company at risk, financially and operationally.

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The right DSPM architecture brings efficiency, not just raw scale. Sentra delivers real-time, context-rich security posture with far greater efficiency, so your protection and your cloud spending can keep up with your growing business. Security needs to grow along with scale. Rising costs and new risks shouldn’t grow right alongside it.

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Want to see how your current petabyte security posture compares? Schedule a demo and see Sentra’s efficiency for yourself.

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Atlassian tools such as Jira and Confluence are the beating heart of software development and IT operations. They power everything from sprint planning to debugging production issues. But behind their convenience lies a less-visible problem: these collaboration platforms quietly accumulate vast amounts of sensitive data often over years that security teams can’t easily monitor or control.

The Problem: Sensitive Data Hidden in Plain Sight

Many organizations rely on Jira to manage tickets, track incidents, and communicate across teams. But within those tickets and attachments lies a goldmine of sensitive information:

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• Credentials and access keys to different environments.
  
• Intellectual property, including code snippets and architecture diagrams.
  
• Production data used to reproduce bugs or validate fixes — often in violation of data-handling regulations.
  
• Real customer records shared for troubleshooting purposes.
  
  

This accumulation isn’t deliberate; it’s a natural byproduct of collaboration. However, it results in a long-tail exposure risk - historical tickets that remain accessible to anyone with permissions.

The Insider Threat Dimension

Because Jira and Confluence retain years of project history, employees and contractors may have access to data they no longer need. In some organizations, teams include offshore or external contributors, multiplying the risk surface. Any of these users could intentionally or accidentally copy or export sensitive content at any moment.

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Why Sensitive Data Is So Hard to Find

Sensitive data in Atlassian products hides across three levels, each requiring a different detection approach:

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1. Structured Data (Records): Every ticket or page includes structured fields - reporter, status, labels, priority. These schemas are customizable, meaning sensitive fields can appear unpredictably. Security teams rarely have visibility or consistent metadata across instances.
   
   
2. Unstructured Data (Descriptions & Discussions): Free-text fields are where developers collaborate — and where secrets often leak. Comments can contain access tokens, internal URLs, or step-by-step guides that expose system details.
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3. Unstructured Data (Attachments): Screenshots, log files, spreadsheets, code exports, or even database snapshots are commonly attached to tickets. These files may contain credentials, customer PII, or proprietary logic, yet they are rarely scanned or governed.

The Challenge for Security Teams

Traditional security tools were never designed for this kind of data sprawl. Atlassian environments can contain millions of tickets and pages, spread across different projects and permissions. Manually auditing this data is impractical. Even modern DLP tools struggle to analyze the context of free text or attachments embedded within these platforms.

Compliance teams face an uphill battle: GDPR, HIPAA, and SOC 2 all require knowing where sensitive data resides. Yet in most Atlassian instances, that visibility is nonexistent.

How Sentra Solves the Problem

Sentra takes a different approach. Its cloud-native data security platform discovers and classifies sensitive data wherever it lives - across SaaS applications, cloud storage, and on-prem environments. When connecting your atlassian environment, Sentra delivers visibility and control across every layer of Jira and Confluence.

Comprehensive Coverage

Sentra delivers consistent data governance across SaaS and cloud-native environments. When connected to Atlassian Cloud, Sentra’s discovery engine scans Jira and Confluence content to uncover sensitive information embedded in tickets, pages, and attachments, ensuring full visibility without impacting performance.

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In addition, Sentra’s flexible architecture can be extended to support hybrid environments, providing organizations with a unified view of sensitive data across diverse deployment models.

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AI-Based Classification

Using advanced AI models, Sentra classifies data across all three tiers:

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• Structured metadata, identifying risky fields and tags.
  
• Unstructured text, analyzing ticket descriptions, comments, and discussions for credentials, PII, or regulated data.
  
• Attachments, scanning files like logs or database snapshots for hidden secrets.
  
  

This contextual understanding distinguishes between harmless content and genuine exposure, reducing false positives.

Full Lifecycle Scanning

Sentra doesn’t just look at new tickets, it scans the entire historical archive to detect legacy exposure, while continuously monitoring for ongoing changes. This dual approach helps security teams remediate existing risks and prevent future leaks.

The Real-World Impact

Organizations using Sentra gain the ability to:

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• Prevent accidental leaks of credentials or production data in collaboration tools.
  
• Enforce compliance by mapping sensitive data across Jira and Confluence.
  
• Empower DevOps and security teams to collaborate safely without stifling productivity.

Conclusion

Collaboration is essential, but it should never compromise data security. Atlassian products enable innovation and speed, yet they also hold years of unmonitored information. Sentra bridges that gap by giving organizations the visibility and intelligence to discover, classify, and protect sensitive data wherever it lives, even in Jira and Confluence.

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Unstructured data is the fastest-growing, least-governed, and most dangerous class of enterprise data. Emails, Slack messages, PDFs, screenshots, presentations, code repositories, logs, and the endless stream of GenAI-generated content — this is where the real risk lives.

The Unstructured data dilemma is this: 80% of your organization’s data is essentially invisible to your current security tools, and the volume is climbing by up to 65% each year. This isn’t just a hypothetical - it’s the reality for enterprises as unstructured data spreads across cloud and SaaS platforms. Yet, most Data Security Posture Management (DSPM) solutions - often called DSPM 1.0 - were never built to handle this explosion at petabyte scale. Especially legacy vendors and first-generation players like Cyera — were never designed to handle unstructured data at scale. Their architectures, classification engines, and scanning models break under real enterprise load.

Looking ahead to 2026, unstructured data security risk stands out as the single largest blind spot in enterprise security. If overlooked, it won’t just cause compliance headaches and soaring breach costs - it could put your organization in the headlines for all the wrong reasons.

The 80% Problem: Unstructured Data Dominates Your Risk

The Scale You Can’t Ignore - Over 80% of enterprise data is unstructured
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• Unstructured data is growing 55-65% per year; by 2025, the world will store more than 180 zettabytes of it.
• 95% of organizations say unstructured data management is a critical challenge but less than 40% of data security budgets address this high-risk area. Unstructured data is everywhere: cloud object stores, SaaS apps, collaboration tools, and legacy file shares. Unlike structured data in databases, it often lacks consistent metadata, access controls, or even basic visibility. This “dark data” is behind countless breaches, from accidental file exposures and overshared documents to sensitive AI training datasets left unmonitored.

The Business Impact - The average breach now costs $4-4.9M, with unstructured data often at the center.

• Poor data quality, mostly from unstructured sources, costs the U.S. economy $3.1 trillion each year.
• More than half of organizations report at least one non-compliance incident annually, with average costs topping $1M. The takeaway: Unstructured data isn’t just a storage problem.

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Why DSPM 1.0 Fails: The Blind Spots of Legacy Approaches

Traditional Tools Fall Short in Cloud-First, Petabyte-Scale Environments

Legacy DSPM and DCAP solutions, such as Varonis or Netwrix - were built for an era when data lived on-premises, followed predictable structures, and grew at a manageable pace.

In today’s cloud-first reality, their limitations have become impossible to ignore:
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• Discovery Gaps: Agent-based scanning can’t keep up with sprawling, constantly changing cloud and SaaS environments. Shadow and dark data across platforms like Google Drive, Dropbox, Slack, and AWS S3 often go unseen.
• Performance Limits: Once environments exceed 100 TB, and especially as they reach petabyte scale—these tools slow dramatically or miss data entirely.
• Manual Classification: Most legacy tools rely on static pattern matching and keyword rules, causing them to miss sensitive information hidden in natural language, code, images, or unconventional file formats.
• Limited Automation: They generate alerts but offer little or no automated remediation, leaving security teams overwhelmed and forcing manual cleanup.
• Siloed Coverage: Solutions designed for on-premises or single-cloud deployments create dangerous blind spots as organizations shift to multi-cloud and hybrid architectures.

Example: Collaboration App Exposure

A global enterprise recently discovered thousands of highly sensitive files—contracts, intellectual property, and PII—were unintentionally shared with “anyone with the link” inside a cloud collaboration platform. Their legacy DSPM tool failed to identify the exposure because it couldn’t scan within the app or detect real-time sharing changes.

Further, even Emerging DSPM tools often rely on pattern matching or LLM-based scanning. These approaches also fail for three reasons:

• Inaccuracy at scale: LLMs hallucinate, mislabel, and require enormous compute.
• Cost blow-ups: Vendors pass massive cloud bills back to customers or incur inordinate compute cost.
• Architectural limitations: Without clustering and elastic scaling, large datasets overwhelm the system.

This is exactly where Cyera and legacy tools struggle - and where Sentra’s SLM-powered classifier thrives with >99% accuracy at a fraction of the cost.

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The New Mandate: Securing Unstructured Data in 2026 and Beyond

GenAI, and stricter privacy laws (GDPR, CCPA, HIPAA) have raised the stakes for unstructured data security. Gartner now recommends Data Access Governance (DAG) and AI-driven classification to reduce oversharing and prepare for AI-centric workloads.
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What Modern Security Leaders Need - Agentless, Real-Time Discovery: No deployment hassles, continuous visibility, and coverage for unstructured data stores no matter where they live.

• Petabyte-Scale Performance: Scan, classify, and risk-score all data, everywhere it lives.
• AI-Driven Deep Classification: Use of natural language processing (NLP), Domain-specific  Small Language Models (SLMs), and context analysis for every unstructured format.
• Automated Remediation: Playbooks that fix exposures, govern permissions, and ensure compliance without manual work.
• Multi-Cloud & SaaS Coverage: Security that follows your data, wherever it goes.

Sentra: Turning the 80% Blind Spot into a Competitive Advantage

Sentra was built specifically to address the risks of unstructured data in 2026 and beyond. There are nuances involved in solving this.  Selecting an appropriate solution is key to a sustainable approach. Here’s what sets Sentra apart:
 

• Agentless Discovery Across All Environments:Instantly scans and classifies unstructured data across AWS, Azure, Google, M365, Dropbox, legacy file shares, and more - no agents required, no blind spots left behind.
• Petabyte-Tested Performance:Designed for Fortune 500 scale, Sentra keeps speed and accuracy high across petabytes, not just terabytes.
• AI-Powered Deep Classification:Our platform uses advanced NLP, SLMs, and context-aware algorithms to classify, label, and risk-score every file - including code, images, and AI training data, not just structured fields.
• Continuous, Context-Rich Visibility:Real-time risk scoring, identity and access mapping, and automated data lineage show not just where data lives, but who can access it and how it’s used.
• Automated Remediation and Orchestration: Sentra goes beyond alerts. Built-in playbooks fix permissions, restrict sharing, and enforce policies within seconds.
• Compliance-First, Audit-Ready: Quickly spot compliance gaps, generate audit trails, and reduce regulatory risk and reporting costs.     

During a recent deployment with a global financial services company, Sentra uncovered 40% more exposed sensitive files than their previous DSPM tool. Automated remediation covered over 10 million documents across three clouds, cutting manual investigation time by 80%.

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Actionable Takeaways for Security Leaders 

1. Put Unstructured Data at the Center of Your 2026 Security Plan: Make sure your DSPM strategy covers all data, especially “dark” and shadow data in SaaS, object stores, and collaboration platforms.

2.  Choose Agentless, AI-Driven Discovery: Legacy, agent-based tools can’t keep up. And underperforming emerging tools may not adequately scale.  Look for continuous, automated scanning and classification that scales with your data.

3.  Automate Remediation Workflows: Visibility is just the start; your platform should fix exposures and enforce policies in real time.

4.  Adopt Multi-Cloud, SaaS-Agnostic Solutions: Your data is everywhere, and your security should be too. Ensure your solution supports all of your unstructured data repositories.

5.  Make Compliance Proactive: Use real-time risk scoring and automated reporting to stay ahead of auditors and regulators.

Conclusion: Ready for the 80% Challenge? ‍

With petabyte-scale, cloud-first data, ignoring unstructured data risk is no longer an option. Traditional DSPM tools can’t keep up, leaving most of your data - and your business - vulnerable. Sentra’s agentless, AI-powered platform closes this gap, delivering the discovery, classification, and automated response you need to turn your biggest blind spot into your strongest defense. See how Sentra uncovers your hidden risk - book an instant demo today.

Don’t let unstructured data be your organization’s Achilles’ heel. With Sentra, enterprises finally have a way to secure the data that matters most.

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The recent exposure of customer data through a compromised Gainsight integration within Salesforce environments is more than an isolated event - it’s a sign of a rapidly evolving class of SaaS supply-chain threats. Even trusted AppExchange partners can inadvertently create access pathways that attackers exploit, especially when OAuth tokens and machine-to-machine connections are involved. This post explores what happened, why today’s security tooling cannot fully address this scenario, and how data-centric visibility and identity governance can meaningfully reduce the blast radius of similar breaches.

A Recap of the Incident

In this case, attackers obtained sensitive credentials tied to a Gainsight integration used by multiple enterprises. Those credentials allowed adversaries to generate valid OAuth tokens and access customer Salesforce orgs, in some cases with extensive read capabilities. Neither Salesforce nor Gainsight intentionally misconfigured their systems. This was not a product flaw in either platform. Instead, the incident illustrates how deeply interconnected SaaS environments have become and how the security of one integration can impact many downstream customers.

Understanding the Kill Chain: From Stolen Secrets to Salesforce Lateral Movement

The attackers’ pathway followed a pattern increasingly common in SaaS-based attacks. It began with the theft of secrets; likely API keys, OAuth client secrets, or other credentials that often end up buried in repositories, CI/CD logs, or overlooked storage locations. Once in hand, these secrets enabled the attackers to generate long-lived OAuth tokens, which are designed for application-level access and operate outside MFA or user-based access controls.

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What makes OAuth tokens particularly powerful is that they inherit whatever permissions the connected app holds. If an integration has broad read access, which many do for convenience or legacy reasons, an attacker who compromises its token suddenly gains the same level of visibility. Inside Salesforce, this enabled lateral movement across objects, records, and reporting surfaces far beyond the intended scope of the original integration. The entire kill chain was essentially a progression from a single weakly-protected secret to high-value data access across multiple Salesforce tenants.

Why Traditional SaaS Security Tools Missed This

Incident response teams quickly learned what many organizations are now realizing: traditional CASBs and CSPMs don’t provide the level of identity-to-data context necessary to detect or prevent OAuth-driven supply-chain attacks.

CASBs primarily analyze user behavior and endpoint connections, but OAuth apps are “non-human identities” - they don’t log in through browsers or trigger interactive events. CSPMs, in contrast, focus on cloud misconfigurations and posture, but they don’t understand the fine-grained data models of SaaS platforms like Salesforce. What was missing in this incident was visibility into how much sensitive data the Gainsight connector could access and whether the privileges it held were appropriate or excessive. Without that context, organizations had no meaningful way to spot the risk until the compromise became public.

Sentra Helps Prevent and Contain This Attack Pattern

Sentra’s approach is fundamentally different because it starts with data: what exists, where it resides, who or what can access it, and whether that access is appropriate. Rather than treating Salesforce or other SaaS platforms as black boxes, Sentra maps the data structures inside them, identifies sensitive records, and correlates that information with identity permissions including third-party apps, machine identities, and OAuth sessions.

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One key pillar of Sentra’s value lies in its DSPM capabilities. The platform identifies sensitive data across all repositories, including cloud storage, SaaS environments, data warehouses, code repositories, collaboration platforms, and even on-prem file systems. Because Sentra also detects secrets such as API keys, OAuth credentials, private keys, and authentication tokens across these environments, it becomes possible to catch compromised or improperly stored secrets before an attacker ever uses them to access a SaaS platform.

Another area where this becomes critical is the detection of over-privileged connected apps. Sentra continuously evaluates the scopes and permissions granted to integrations like Gainsight, identifying when either an app or an identity holds more access than its business purpose requires. This type of analysis would have revealed that a compromised integrated app could see far more data than necessary, providing early signals of elevated risk long before an attacker exploited it.

Sentra further tracks the health and behavior of non-human identities. Service accounts and connectors often rely on long-lived credentials that are rarely rotated and may remain active long after the responsible team has changed. Sentra identifies these stale or overly permissive identities and highlights when their behavior deviates from historical norms. In the context of this incident type, that means detecting when a connector suddenly begins accessing objects it never touched before or when large volumes of data begin flowing to unexpected locations or IP ranges.

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Finally, Sentra’s behavior analytics (part of DDR) help surface early signs of misuse. Even if an attacker obtains valid OAuth tokens, their data access patterns, query behavior, or geography often diverge from the legitimate integration. By correlating anomalous activity with the sensitivity of the data being accessed, Sentra can detect exfiltration patterns in real time—something traditional tools simply aren’t designed to do.

The 2026 Outlook: More Incidents Are Coming

The Gainsight/Salesforce incident is unlikely to be the last of its kind. The speed at which enterprises adopt SaaS integrations far exceeds the rate at which they assess the data exposure those integrations create. OAuth-based supply-chain attacks are growing quickly because they allow adversaries to compromise one provider and gain access to dozens or hundreds of downstream environments. Given the proliferation of partner ecosystems, machine identities, and unmonitored secrets, this attack vector will continue to scale.

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Prediction:
Unless enterprises add data-centric SaaS visibility and identity-aware DSPM, we should expect three to five more incidents of similar magnitude before summer 2026.

Conclusion

The real lesson from the Gainsight/Salesforce breach is not to reduce reliance on third-party SaaS providers as modern business would grind to a halt without them. The lesson is that enterprises must know where their sensitive data lives, understand exactly which identities and integrations can access it, and ensure those privileges are continuously validated. Sentra provides that visibility and contextual intelligence, making it possible to identify the risks that made this breach possible and help to prevent the next one.

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The modern enterprise runs on collaboration and nothing powers that more than Microsoft 365. From Exchange Online and OneDrive to SharePoint, Teams, and Copilot workflows, M365 hosts a massive and ever-growing volume of unstructured content: documents, presentations, spreadsheets, image files, chats, attachments, and more.

Yet unstructured = harder to govern. Unlike tidy database tables with defined schemas, unstructured repositories flood in with ambiguous content types, buried duplicates, or unused legacy files. It’s in these stacks that sensitive IP, model training data, or derivative work can quietly accumulate, and then leak.

Consider this: one recent study found that more than 81 % of IT professionals report data-loss events in M365 environments. And to make matters worse, according to the International Data Corporation (IDC), 60% of organizations do not have a strategy for protecting their critical business data that resides in Microsoft 365.

Why Traditional Tools Struggle

• Built-in classification tools (e.g., M365’s native capabilities) often rely on pattern matching or simple keywords, and therefore struggle with accuracy, context, scale and derivative content.
  
  
• Many solutions only surface that a file exists and carries a type label - but stop short of mapping who or what can access it, its purpose, and what its downstream exposure might be.
  
  
• GenAI workflows now pump massive volumes of unstructured data into copilots, knowledge bases, training sets - creating new blast radii that legacy DLP or labeling tools weren’t designed to catch.
  

What a Modern Platform Must Deliver

1. High-accuracy, petabyte-scale classification of unstructured data (so you know what you have, where it sits, and how sensitive it is). And it must keep pace with explosive data growth and do so cost efficiently.
   
   
2. Unified Data Access Governance (DAG) - mapping identities (users, service principals, agents), permissions, implicit shares, federated/cloud-native paths across M365 and beyond.
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3. Data Detection & Response (DDR) - continuous monitoring of data movement, copies, derivative creation, AI agent interactions, and automated response/remediation.

How Sentra addresses this in M365

At Sentra, we’ve built a cloud-native data-security platform specifically to address this triad of capabilities - and we extend that deeply into M365 (OneDrive, SharePoint, Teams, Exchange Online) and other SaaS platforms.

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• A newly announced AI Classifier for Unstructured Data accelerates and improves classification across M365’s unstructured repositories (see: Sentra launches breakthrough unstructured-data AI classification capabilities).
  
  
• Petabyte-scale processing: our architecture supports classification and monitoring of massive file estates without astronomical cost or time-to-value.
  
  
• Seamless support for M365 services: read/write access, ingestion, classification, access-graph correlation, detection of shadow/unmanaged copies across OneDrive and SharePoint—plus integration into our DAG and DDR layers (see our guide: How to Secure Regulated Data in Microsoft 365 + Copilot).
  
  
• Cost-efficient deployment: designed for high scale without breaking the budget or massive manual effort.
  

The Bottom Line

In today’s cloud/AI era, saying “we discovered the PII in my M365 tenant” isn’t enough.

The real question is: Do I know who or what (user/agent/app) can access that content, what its business purpose is, and whether it’s already been copied or transformed into a risk vector?

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If your solution can’t answer that, your unstructured data remains a silent, high-stakes liability and resolving concerns becomes a very costly, resource-draining burden. By embracing a platform that combines classification accuracy, petabyte-scale processing, unified DSPM + DAG + DDR, and deep M365 support, you move from “hope I’m secure” to “I know I’m secure.”

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Want to see how it works in a real M365 setup? Check out our video or book a demo.

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Every organization today is drowning in data - millions of assets spread across cloud platforms, on-premises systems, and an ever-expanding landscape of SaaS tools. Each asset carries value, but also risk. For security and compliance teams, the mandate is clear: sensitive data must be inventoried, managed and protected.

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Scanning every asset for security and compliance is no longer optional, it’s the line between trust and exposure, between resilience and chaos.

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Many data security tools promise to scan and classify sensitive information across environments. In practice, doing this effectively and at scale, demands more than raw ‘brute force’ scanning power. It requires robust visibility and management capabilities: a cockpit view that lets teams monitor coverage, prioritize intelligently, and strike the right balance between scan speed, cost, and accuracy.

Why Scan Tracking Is Crucial

Scanning is not instantaneous. Depending on the size and complexity of your environment, it can take days - sometimes even weeks to complete. Meanwhile, new data is constantly being created or modified, adding to the challenge.

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Without clear visibility into the scanning process, organizations face several critical obstacles:

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• Unclear progress: It’s often difficult to know what has already been scanned, what is currently in progress, and what remains pending. This lack of clarity creates blind spots that undermine confidence in coverage.
  
  
• Time estimation gaps: In large environments, it’s hard to know how long scans will take because so many factors come into play — the number of assets, their size, the type of data - structured, semi-structured, or unstructured, and how much scanner capacity is available. As a result, predicting when you’ll reach full coverage is tricky. This becomes especially stressful when scans need to be completed before a fixed deadline, like a compliance audit. 
  
  "With Sentra’s Scan Dashboard, we were able to quickly scale up our scanners to meet a tight audit deadline, finish on time, and then scale back down to save costs. The visibility and control it gave us made the whole process seamless”, said CISO of Large Retailer.‍
• Poor prioritization: Not all environments or assets carry the same importance. Yet without visibility into scan status, teams struggle to balance historical scans of existing assets with the ongoing influx of newly created data, making it nearly impossible to prioritize effectively based on risk or business value.

Sentra’s End-to-End Scanning Workflow

Managing scans at petabyte scale is complex. Sentra streamlines the process with a workflow built for scale, clarity, and control that features:

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1. Comprehensive Asset Discovery

Before scanning even begins, Sentra automatically discovers assets across cloud platforms, on-premises systems, and SaaS applications. This ensures teams have a complete, up-to-date inventory and visual map of their data landscape, so no environment or data store is overlooked.

Example: New S3 buckets, a freshly deployed BigQuery dataset, or a newly connected SharePoint site are automatically identified and added to the inventory.
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2. Configurable Scan Management

Administrators can fine-tune how scans are executed to meet their organization’s needs. With flexible configuration options, such as number of scanners, sampling rates, and prioritization rules - teams can strike the right balance between scan speed, coverage, and cost control.

For instance, compliance-critical assets can be scanned at full depth immediately, while less critical environments can run at reduced sampling to save on compute consumption and costs.

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3. Real-Time Scan Dashboard

Sentra’s unified Scan Dashboard provides a cockpit view into scanning operations, so teams always know where they stand. Key features include:

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• Daily scan throughput correlated with the number of active scanners, helping teams understand efficiency and predict completion times.
• Coverage tracking that visualizes overall progress and highlights which assets remain unscanned.
• Decision-making tools that allow teams to dynamically adjust, whether by adding scanner capacity, changing sampling rates, or reordering priorities when new high-risk assets appear.

Handling Data Changes

The challenge doesn’t end once the initial scans are complete. Data is dynamic, new files are added daily, existing records are updated, and sensitive information shifts locations. Sentra’s activity feeds give teams the visibility they need to understand how their data landscape is evolving and adapt their data security strategies in real time.

Conclusion

Tracking scan status at scale is complex but critical to any data security strategy. Sentra provides an end-to-end view and unmatched scan control, helping organizations move from uncertainty to confidence with clear prediction of scan timelines, faster troubleshooting, audit-ready compliance, and smarter, cost-efficient decisions for securing data.

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Enhanced DSPM Adoption Is the Most Important Data Security Trend of 2026

Over the past few years, organizations have realized that traditional security tools can’t keep pace with how data moves and grows today. Exploding volumes of sensitive data now flourish across multi-cloud environments, SaaS platforms, and AI systems, often without full visibility by the teams responsible for securing it. Unstructured data presents the greatest risk - representing over 80% of corporate data.

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That’s why Data Security Posture Management (DSPM) has become a critical part of the modern security stack. DSPM tools help organizations automatically discover, classify, monitor, and protect sensitive data - no matter where it lives or travels.

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But in 2026, the data security game is changing. Many DSPMs can tell you what your data is,  but more is needed. Leading DSPM platforms are going beyond visibility. They’re delivering real-time AI-enhanced contextual business insights, automated remediation, and AI-aware accurate protection that scales with your dynamic data.

AI-enhanced DSPM Capabilities in 2026

Not all DSPM tools are built the same. The top platforms share a few key traits that define the next generation of data security posture management:

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Top DSPM Tools to Watch in 2026

Based on recent analyst coverage, market growth, and innovation across the industry, here are the top DSPM platforms to watch this year, each contributing to how data security is evolving.

1. Sentra

As a cloud-native DSPM platform, Sentra focuses on continuous data protection, not just visibility. It discovers and accurately classifies sensitive data in real time across all cloud environments, while automatically remediating risks through policy-driven automation.

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What sets Sentra apart:

• Continuous, automated discovery and classification across your entire data estate - cloud, SaaS, and on-premises.
• Business Contextual insights that understand the purpose of data, accurately linking data, identity, and risk.
• Automatic learning to discern customer unique data types and continuously improve labeling over time.
• Petabyte scaling and low compute consumption for 10X cost efficiency.
• Automated remediation workflows and integrations to fix issues instantly.
• Built-in coverage for data flowing through AI and SaaS ecosystems.

Ideal for: Security teams looking for a cloud-native DSPM platform built for scalability in the AI era with automation at its core.

2. BigID

A pioneer in data discovery and classification, BigID bridges DSPM and privacy governance, making it a good choice for compliance-heavy sectors.

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Ideal for: Organizations prioritizing data privacy, governance, and audit readiness.

3. Prisma Cloud (Palo Alto Networks)

Prisma’s DSPM offering integrates closely with CSPM and CNAPP components, giving security teams a single pane of glass for infrastructure and data risk.

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Ideal for: Enterprises with hybrid or multi-cloud infrastructures already using Palo Alto tools.

4. Microsoft Purview / Defender DSPM

Microsoft continues to invest heavily in DSPM through Purview, offering rich integration with Microsoft 365 and Azure ecosystems. Note: Sentra integrates with Microsoft Purview Information Protection (MPIP) labeling and DLP policies.

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Ideal for: Microsoft-centric organizations seeking native data visibility and compliance automation.

5. Securiti.ai

Positioned as a “Data Command Center,” Securiti unifies DSPM, privacy, and governance. Its strength lies in automation and compliance visibility and SaaS coverage.

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Ideal for: Enterprises looking for an all-in-one governance and DSPM solution.

6. Cyera

Cyera has gained attention for serving the SMB segment with its DSPM approach. It uses LLMs for data context, supplementing other classification methods, and provides integrations to IAM and other workflow tools.

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Ideal for: Small/medium growing companies that need basic DSPM functionality.

7. Wiz

Wiz continues to lead in cloud security, having added DSPM capabilities into its CNAPP platform. They’re known for deep multi-cloud visibility and infrastructure misconfiguration detection.

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Ideal for: Enterprises running complex cloud environments looking for infrastructure vulnerability and misconfiguration management.

8. Varonis

Varonis remains a strong player for hybrid and on-prem data security, with deep expertise in permissions and access analytics and focus on SaaS/unstructured data.

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Ideal for: Enterprises with legacy file systems or mixed cloud/on-prem architectures.

9. Netwrix

Netwrix’s platform incorporates DSPM-related features into its auditing and access control suite.
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‍Ideal for: Mid-sized organizations seeking DSPM as part of a broader compliance solution.

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Emerging DSPM Trends to Watch in 2026

1. AI Data Security: As enterprises adopt GenAI, DSPM tools are evolving to secure data used in training and inference.
   
   
2. Identity-Centric Risk: Understanding and controlling both human and machine identities is now central to data posture.
   
   
3. Automation-Driven Security: Remediation workflows are becoming the differentiator between “good” and “great.”
   
   

Market Consolidation: Expect to see CNAPP, legacy security, and cloud vendors acquiring DSPM startups to strengthen their coverage.

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How to Choose the Right DSPM Tool

When evaluating a DSPM solution, align your choice with your data landscape and goals:

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• Cloud-Native Company Choose tools designed for cloud-first environments (like Sentra, Securiti, Wiz).
  
• Compliance Priority Platforms like Sentra, BigID or Securiti excel in privacy and governance.
  
• Microsoft-Heavy Stack Purview and Sentra DSPM offer native integration.
  
• Hybrid Environment Consider Varonis, Prisma Cloud, or Sentra for extended visibility.
  
• Enterprise Scalability Evaluate deployment ease, petabyte scalability, cloud resource consumption, scanning efficiency, etc. (Sentra excels here)

*Pro Tip: Run a proof of concept (POC) across multiple environments to test scalability, accuracy, and operational cost effectiveness before full deployment.

Final Thoughts: DSPM Is About Action

The best DSPM tools in 2026 share one core principle, they help organizations move from visibility to action.

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At Sentra, we believe that the future of DSPM lies in continuous, automated data protection:

• Real-time discovery of sensitive data @ scale
• Context-aware prioritization for business insight
• Automated remediation that reduces risk instantly

As data continues to power AI, analytics, and innovation, DSPM ensures that innovation never comes at the cost of security. See how Sentra helps leading enterprises protect data across multi-cloud and SaaS environments.

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The LLM Hype, and What’s Missing

Over the past few years, large language models (LLMs) have dominated the AI conversation. From writing essays to generating code, LLMs like GPT-4 and Claude have proven that massive models can produce human-like language and reasoning at scale.

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But here's the catch: not every task needs a 70-billion-parameter model. Parameters are computationally expensive - they require both memory and processing time.

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At Sentra, we discovered early on that the work our customers rely on for accurate, scalable classification of massive data flows - isn’t about writing essays or generating text. It’s about making decisions fast, reliably, and cost-effectively across dynamic, real-world data environments. While large language models (LLMs) are excellent at solving general problems, it creates a lot of unnecessary computational overhead.

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That’s why we’ve shifted our focus toward Small Language Models (SLMs) - compact, specialized models purpose-built for a single task - understanding and classifying data efficiently. By running hundreds of SLMs in parallel on regular CPUs, Sentra can deliver faster insights, stronger data privacy, and a dramatically lower total cost of AI-based classification that scales with their business, not their cloud bill.

What Is an SLM?

An SLM is a smaller, domain-specific version of a language model. Instead of trying to understand and generate any kind of text, an SLM is trained to excel at a particular task, such as identifying the topic of a document (what the document is about or what type of document it is), or detecting sensitive entities within documents, such as passwords, social security numbers, or other forms of PII.

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In other words: If an LLM is a generalist, an SLM is a specialist. At Sentra, we use SLMs that are tuned and optimized for security data classification, allowing them to process high volumes of content with remarkable speed, consistency, and precision. These SLMs are based on standard open source models, but trained with data that was curated by Sentra, to achieve the level of accuracy that only Sentra can guarantee.

From LLMs to SLMs: A Strategic Evolution

Like many in the industry, we started by testing LLMs to see how well they could classify and label data. They were powerful, but also slow, expensive, and difficult to scale. Over time, it became clear: LLMs are too big and too expensive to run on customer data for Sentra to be a viable, cost effective solution for data classification.

Each SLM handles a focused part of the process: initial categorization, text extraction from documents and images, and sensitive entity classification. The SLMs are not only accurate (even more accurate than LLMs classifying using prompts) - they can run on standard CPUs efficiently, and they run inside the customer’s environment, as part of Sentra’s scanners.

The Benefits of SLMs for Customers

a. Speed and Efficiency

SLMs process data faster because they’re lean by design. They don’t waste cycles generating full sentences or reasoning across irrelevant contexts. This means real-time or near-real-time classification, even across millions of data points.

b. Accuracy and Adaptability

SLMs are pre-trained “zero-shot” language models that can categorize and classify generically, without the need to pre-train on a specific task in advance. This is the meaning of “zero shot” - it means that regardless of the data it was trained on, the model can classify an arbitrary set of entities and document labels without training on each one specifically. This is possible due to the fact that language models are very advanced, and they are able to capture deep natural language understanding at the training stage.

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Regardless of that, Sentra fine tunes these models to further increase the accuracy of the classification, by curating a very large set of tagged data that resembles the type of data that our customers usually run into.

Our feedback loops ensure that model performance only gets better over time - a direct reflection of our customers’ evolving environments.

c. Cost and Sustainability

Because SLMs are compact, they require less compute power, which means lower operational costs and a smaller carbon footprint. This efficiency allows us to deliver powerful AI capabilities to customers without passing on the heavy infrastructure costs of running massive models.

d. Security and Control

Unlike LLMs hosted on external APIs, SLMs can be run within Sentra’s secure environment, preserving data privacy and regulatory compliance. Customers maintain full control over their sensitive information - a critical requirement in enterprise data security.

A Quick Comparison: SLMs vs. LLMs

The difference between SLMs and LLMs becomes clear when you look at their performance across key dimensions:

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Continuous Learning: How Sentra’s SLMs Grow

One of the most powerful aspects of our SLM approach is continuous learning. Each Sentra customer project contributes valuable insights, from new data patterns to evolving classification needs. These learnings feed back into our training workflows, helping us refine and expand our models over time.

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While not every model retrains automatically, the system is built to support iterative optimization: as our team analyzes feedback and performance, models can be fine-tuned or extended to handle new categories and contexts.

The result is an adaptive ecosystem of SLMs that becomes more effective as our customer base and data diversity grow, ensuring Sentra’s AI remains aligned with real-world use cases.

Sentra’s Multi-SLM Architecture

Sentra’s scanning technology doesn’t rely on a single model. We run many SLMs in parallel, each specializing in a distinct layer of classification:

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1. Embedding models that convert data into meaningful vector representations
2. Entity Classification models that label sensitive entities
3. Document Classification models that label documents by type
4. Image-to-text and speech-to-text models that are able to process non-textual data into textual data

This layered approach allows us to operate at scale - quickly, cheaply, and with great results. In practice, that means faster insights, fewer errors, and a more responsive platform for every customer.

The Future of AI Is Specialized

We believe the next frontier of AI isn’t about who can build the biggest model, it’s about who can build the most efficient, adaptive, and secure ones.

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By embracing SLMs, Sentra is pioneering a future where AI systems are purpose-built, transparent, and sustainable. Our approach aligns with a broader industry shift toward task-optimized intelligence - models that do one thing extremely well and can learn continuously over time.

Conclusion: The Power of Small

At Sentra, we’ve learned that in AI, bigger isn’t always better. Our commitment to SLMs reflects our belief that efficiency, adaptability, and precision matter most for customers. By running thousands of small, smart models rather than a single massive one, we’re able to classify data faster, cheaper, and with greater accuracy - all while ensuring customer privacy and control.

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In short: Sentra’s SLMs represent the power of small, and the future of intelligent classification.

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In the burgeoning realm of artificial intelligence (AI), Large Language Models (LLMs) have emerged as transformative tools, enabling the development of applications that revolutionize customer experiences and streamline business operations. These sophisticated models, trained on massive volumes of text data, can generate human-quality text, translate languages, write creative content, and answer complex questions.

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Unfortunately, the rapid adoption of LLMs - coupled with their extensive data consumption - has introduced critical challenges around data integrity, privacy, and access control during both training and inference. As organizations operationalize LLMs at scale in 2025, addressing these risks has become essential to responsible AI adoption.

What’s Changed in LLM Security in 2025

LLM security in 2025 looks fundamentally different from earlier adoption phases. While initial concerns focused primarily on prompt injection and output moderation, today’s risk profile is dominated by data exposure, identity misuse, and over-privileged AI systems.

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Several shifts now define the modern LLM security landscape:

• Retrieval-augmented generation (RAG) has become the default architecture, dynamically connecting LLMs to internal data stores and increasing the risk of sensitive data exposure at inference time.
• Fine-tuning and continual training on proprietary data are now common, expanding the blast radius of data leakage or poisoning incidents.
• Agentic AI and tool-calling capabilities introduce new attack surfaces, where excessive permissions can enable unintended actions across cloud services and SaaS platforms.
• Multi-model and hybrid AI environments complicate data governance, access control, and visibility across LLM workflows.

As a result, securing LLMs in 2025 requires more than static policies or point-in-time reviews. Organizations must adopt continuous data discovery, least-privilege access enforcement, and real-time monitoring to protect sensitive data throughout the LLM lifecycle.

Challenges: Navigating the Risks of LLM Training

Against this backdrop, the training of LLMs often involves the use of vast datasets containing sensitive information such as personally identifiable information (PII), intellectual property, and financial records. This concentration of valuable data presents a compelling target for malicious actors seeking to exploit vulnerabilities and gain unauthorized access.

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One of the primary challenges is preventing data leakage or public disclosure. LLMs can inadvertently disclose sensitive information if not properly configured or protected. This disclosure can occur through various means, such as unauthorized access to training data, vulnerabilities in the LLM itself, or improper handling of user inputs.

Another critical concern is avoiding overly permissive configurations. LLMs can be configured to allow users to provide inputs that may contain sensitive information. If these inputs are not adequately filtered or sanitized, they can be incorporated into the LLM's training data, potentially leading to the disclosure of sensitive information.

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Finally, organizations must be mindful of the potential for bias or error in LLM training data. Biased or erroneous data can lead to biased or erroneous outputs from the LLM, which can have detrimental consequences for individuals and organizations.

‍OWASP Top 10 for LLM Applications

The OWASP Top 10 for LLM Applications identifies and prioritizes critical vulnerabilities that can arise in LLM applications. Among these, LLM03 Training Data Poisoning, LLM06 Sensitive Information Disclosure, LLM08 Excessive Agency, and LLM10 Model Theft pose significant risks that cybersecurity professionals must address. Let's dive into these:

LLM03: Training Data Poisoning

LLM03 addresses the vulnerability of LLMs to training data poisoning, a malicious attack where carefully crafted data is injected into the training dataset to manipulate the model's behavior. This can lead to biased or erroneous outputs, undermining the model's reliability and trustworthiness.

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The consequences of LLM03 can be severe. Poisoned models can generate biased or discriminatory content, perpetuating societal prejudices and causing harm to individuals or groups. Moreover, erroneous outputs can lead to flawed decision-making, resulting in financial losses, operational disruptions, or even safety hazards.

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LLM06: Sensitive Information Disclosure

LLM06 highlights the vulnerability of LLMs to inadvertently disclosing sensitive information present in their training data. This can occur when the model is prompted to generate text or code that includes personally identifiable information (PII), trade secrets, or other confidential data.

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The potential consequences of LLM06 are far-reaching. Data breaches can lead to financial losses, reputational damage, and regulatory penalties. Moreover, the disclosure of sensitive information can have severe implications for individuals, potentially compromising their privacy and security.

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LLM08: Excessive Agency

LLM08 focuses on the risk of LLMs exhibiting excessive agency, meaning they may perform actions beyond their intended scope or generate outputs that cause harm or offense. This can manifest in various ways, such as the model generating discriminatory or biased content, engaging in unauthorized financial transactions, or even spreading misinformation.

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Excessive agency poses a significant threat to organizations and society as a whole. Supply chain compromises and excessive permissions to AI-powered apps can erode trust, damage reputations, and even lead to legal or regulatory repercussions. Moreover, the spread of harmful or offensive content can have detrimental social impacts.

LLM10: Model Theft

LLM10 highlights the risk of model theft, where an adversary gains unauthorized access to a trained LLM or its underlying intellectual property. This can enable the adversary to replicate the model's capabilities for malicious purposes, such as generating misleading content, impersonating legitimate users, or conducting cyberattacks.

Model theft poses significant threats to organizations. The loss of intellectual property can lead to financial losses and competitive disadvantages. Moreover, stolen models can be used to spread misinformation, manipulate markets, or launch targeted attacks on individuals or organizations.

Recommendations: Adopting Responsible Data Protection Practices

To mitigate the risks associated with LLM training data, organizations must adopt a comprehensive approach to data protection. This approach should encompass data hygiene, policy enforcement, access controls, and continuous monitoring.

Data hygiene is essential for ensuring the integrity and privacy of LLM training data. Organizations should implement stringent data cleaning and sanitization procedures to remove sensitive information and identify potential biases or errors.

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Policy enforcement is crucial for establishing clear guidelines for the handling of LLM training data. These policies should outline acceptable data sources, permissible data types, and restrictions on data access and usage.

Access controls should be implemented to restrict access to LLM training data to authorized personnel and identities only, including third party apps that may connect. This can be achieved through role-based access control (RBAC), zero-trust IAM, and multi-factor authentication (MFA) mechanisms.

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Continuous monitoring is essential for detecting and responding to potential threats and vulnerabilities. Organizations should implement real-time monitoring tools to identify suspicious activity and take timely action to prevent data breaches.

Solutions: Leveraging Technology to Safeguard Data

In the rush to innovate, developers must remain keenly aware of the inherent risks involved with training LLMs if they wish to deliver responsible, effective AI that does not jeopardize their customer's data.  Specifically, it is a foremost duty to protect the integrity and privacy of LLM training data sets, which often contain sensitive information.

Preventing data leakage or public disclosure, avoiding overly permissive configurations, and negating bias or error that can contaminate such models should be top priorities.

Technological solutions play a pivotal role in safeguarding data integrity and privacy during LLM training. Data security posture management (DSPM) solutions can automate data security processes, enabling organizations to maintain a comprehensive data protection posture.

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DSPM solutions provide a range of capabilities, including data discovery, data classification, data access governance (DAG), and data detection and response (DDR). These capabilities help organizations identify sensitive data, enforce access controls, detect data breaches, and respond to security incidents.

Cloud-native DSPM solutions offer enhanced agility and scalability, enabling organizations to adapt to evolving data security needs and protect data across diverse cloud environments.

Sentra: Automating LLM Data Security Processes

Having to worry about securing yet another threat vector should give overburdened security teams pause. But help is available.

Sentra has developed a data privacy and posture management solution that can automatically secure LLM training data in support of rapid AI application development.

The solution works in tandem with AWS SageMaker, GCP Vertex AI, or other AI IDEs to support secure data usage within ML training activities.  The solution combines key capabilities including DSPM, DAG, and DDR to deliver comprehensive data security and privacy.

Its cloud-native design discovers all of your data and ensures good data hygiene and security posture via policy enforcement, least privilege access to sensitive data, and monitoring and near real-time alerting to suspicious identity (user/app/machine) activity, such as data exfiltration, to thwart attacks or malicious behavior early. The solution frees developers to innovate quickly and for organizations to operate with agility to best meet requirements, with confidence that their customer data and proprietary information will remain protected.

LLMs are now also built into Sentra’s classification engine and data security platform to provide unprecedented classification accuracy for unstructured data. Learn more about Large Language Models (LLMs) here.

Conclusion: Securing the Future of AI with Data Privacy

AI holds immense potential to transform our world, but its development and deployment must be accompanied by a steadfast commitment to data integrity and privacy. Protecting the integrity and privacy of data in LLMs is essential for building responsible and ethical AI applications. By implementing data protection best practices, organizations can mitigate the risks associated with data leakage, unauthorized access, and bias. Sentra's DSPM solution provides a comprehensive approach to data security and privacy, enabling organizations to develop and deploy LLMs with speed and confidence.

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If you want to learn more about Sentra's Data Security Platform and how LLMs are now integrated into our classification engine to deliver unmatched accuracy for unstructured data, request a demo today.

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Finding My Voice: From Kenya to the Global Stage

I was born and raised in Kenya, the youngest of three and the only daughter. My parents, who never had the chance to finish their education, sacrificed everything to give me opportunities they never had. Their courage became my foundation.

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At sixteen, my mother signed me up to speak at a community event, without telling me first! I stood before 500 people and spoke about something that had long bothered me: there were no women on our community board. That same year, two women were appointed for the first time in our community’s history. This year, I was given the recognition for being a Community Leader at the Global Gujrati Gaurav Awards in BC for my work in educating seniors on cyber safety and helping many immigrants secure jobs.

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I didn’t realize it then, but that moment would define my purpose: to speak up for those whose voices aren’t always heard.

From Isolation to Empowerment

When I moved to the UK to study Financial Economics, I faced a different kind of challenge - isolation. My accent made me stand out, and not always in a good way. There were times I felt invisible, even rejected. But I made a promise to myself in those lonely moments that no one else should feel the same way.

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Years later, as a founding member of WiCyS Western Affiliate, I helped redesign how networking happens at cybersecurity events. Instead of leaving it to chance, we introduced structured networking that ensured everyone left with at least one new connection. It was a small change, but it made a big difference. Today, that format has been adopted by organizations like ISC2 and ISACA, creating spaces where every person feels they belong. 

Breaking Barriers and Building SHE

When I pivoted into cybersecurity sales after moving to Canada, I encountered another wall. I applied for a senior role and failed a personality test, one that unfairly filtered out many talented women. I refused to accept that. I focused on listening, solving real customer challenges, and eventually became the top seller. That success helped eliminate the test altogether, opening doors for many more women who came after me. That experience planted a seed that would grow into one of my proudest initiatives: SHE (Sharing Her Empowerment).

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It started as a simple fireside chat on diversity and inclusion - just 40 seats over lunch. Within minutes of sending the invite, we had 90 people signed up. Executives moved us into a larger room, and that event changed everything. SHE became our first employee resource group focused on empowering women, increasing representation in leadership, and amplifying women’s voices within the organization. Even with just 19% women, we created a ripple effect that reached the boardroom and beyond.

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SHE showed me that when women stand together, transformation happens.

Creating Pathways for the Next Generation

Mentorship has always been close to my heart. During the pandemic, I met incredible women, who were trying to break into cybersecurity but kept facing barriers. I challenged hiring norms, advocated for fair opportunities, and helped launch internship programs that gave women hands-on experience. Today, many of them are thriving in their cyber careers, a true reflection of what’s possible when we lift as we climb.

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Through Standout to Lead, I partnered with Women Get On Board to help women in cybersecurity gain board seats. Watching more women step into decision-making roles reminds me that leadership isn’t about titles, it’s about creating pathways for others.

Women in Cybersecurity: Our Collective Story

This year, I’m deeply honored to be named among the Top 20 Cybersecurity Women of the World by the United Cybersecurity Alliance. Their mission - to empower women, elevate diverse voices, and drive equity in our field, mirrors everything I believe in.

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I’m also thrilled to be part of the upcoming documentary premiere, “The WOMEN IN SECURITY Documentary,” proudly sponsored by Sentra, Amazon WWOS, and Pinkerton among others. This film shines a light on the fearless women redefining what leadership looks like in our industry.

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As a member of Sentra’s community, I see the same commitment to visibility, inclusion, and impact that has guided my journey. Together, we’re not just securing data, we’re securing the future of those who will lead next.

Asante Sana – Thank You

My story, my safari, is still being written. I’ve learned that impact doesn’t come from perfection, but from purpose. Whether it’s advocating for fairness, mentoring the next generation, or sharing our stories, every step we take matters.

To every woman, every underrepresented voice in STEM, and everyone who’s ever felt unseen - stay authentic, speak up, and don’t be afraid of the outcome. You might just change the world.

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Join me and the Sentra team at The WOMEN IN SECURITY Documentary Premiere, a celebration of leadership, resilience, and the voices shaping the future of our industry.

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Save your seat at The Women in Security premiere here (spots are limited).

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Follow Sentra on LinkedIn and YouTube for more updates on the event and stories that inspire change.

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Why Data Visibility Belongs in Your 2026 Cybersecurity Budget

As the fiscal year winds down and security leaders tackle cybersecurity budget planning for 2026, you need to decide how to use every remaining 2025 dollar wisely and how to plan smarter for next year. The question isn’t just what to cut or keep, it’s what creates measurable impact. Across programs, data visibility and DSPM deliver provable risk reduction, faster audits, and clearer ROI,making them priority line items whether you’re spending down this year or shaping next year’s plan. Some teams discover unspent funds after project delays, postponed renewals, or slower-than-expected hiring. Others are already deep in planning mode, mapping next year’s security priorities across people, tools, and processes. Either way, one question looms large: where can a limited security budget make the biggest impact - right now and next year?

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Across the industry, one theme is clear: data visibility is no longer a “nice-to-have” line item, it’s a foundational control. Whether you’re allocating leftover funds before year-end or shaping your 2026 strategy, investing in Data Security Posture Management (DSPM) should be part of the plan.

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As Bitsight notes, many organizations look for smart ways to use remaining funds that don’t roll over. The goal isn’t simply to spend, it’s to invest in initiatives that improve posture and provide measurable, lasting value. And according to Applied Tech, “using remaining IT funds strategically can strengthen your position for the next budget cycle.”

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That same principle applies in cybersecurity. Whether you’re closing out this year or planning for 2026, the focus should be on spending that improves security maturity and tells a story leadership understands. Few areas achieve that more effectively than data-centric visibility.

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(For additional background, see Sentra’s article on why DSPM should take a slice of your cybersecurity budget.)

Where to Allocate Remaining Year-End Funds (Without Hurting Next Year’s Budget)

It’s important to utilize all of your 2025 budget allocations because finance departments frequently view underspending as a sign of overfunding, leading to smaller allocations next year. Instead, strategic security teams look for ways to convert every remaining dollar into evidence of progress.

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That means focusing on investments that:

• Produce measurable results you can show to leadership.
  
• Strengthen core program foundations: people, visibility, and process.
  
• Avoid new recurring costs that stretch future budgets.
  

Top Investments That Pay Off

1. Invest in Your People

One of the strongest points echoed by security professionals across industry communities: the best investment is almost always your people. Security programs are built on human capability. Certifications, practical training, and professional growth not only expand your team’s skills but also build morale and retention, two things that can’t be bought with tooling alone.

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High-impact options include:

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• Hands-on training platforms like Hack The Box, INE Skill Dive, or Security Blue Team, which develop real-world skills through simulated environments.
  
• Professional certifications (SANS GIAC, OSCP, or cloud security credentials) that validate expertise and strengthen your team’s credibility.
  
• Conference attendance for exposure to new threat perspectives and networking with peers.
  
• Cross-functional training between SOC, GRC, and AppSec to create operational cohesion.
  
  

In practitioner discussions, one common sentiment stood out: training isn’t just an expense, it’s proof of leadership maturity.

As one manager put it, “If you want your analysts to go the extra mile during an incident, show you’ll go the extra mile for them when things are calm.”

2. Invest in Data Visibility (DSPM)

While team capability drives execution, data visibility drives confidence. In recent conversations among mid-market and enterprise security teams, Data Security Posture Management (DSPM) repeatedly surfaced as one of the most valuable investments made in the past year, especially for hybrid-cloud environments.

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One security leader described it this way:

“After implementing DSPM, we finally had a clear picture of where sensitive data actually lived. It saved our team hours of manual chasing and made the audit season much easier.”

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That feedback reflects a growing consensus: without visibility into where sensitive data resides, who can access it, and how it’s secured, every other layer of defense operates partly in the dark.

*Tip: If your remaining 2025 budget won’t suffice for a full DSPM deployment, you can scope an initial implementation with the remaining budget, then expand to full coverage in 2026.

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DSPM solutions provide that clarity by helping teams:

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• Map and classify sensitive data across multi-cloud and SaaS environments.
  
• Identify access misconfigurations or risky sharing patterns.
  
• Detect policy violations or overexposure before they become incidents.
  
  

Beyond security operations, DSPM delivers something finance and leadership appreciate, measurable proof. Dashboards and reports make risk tangible, allowing CISOs to demonstrate progress in data protection and compliance.

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The takeaway: DSPM isn’t just a good way to use remaining funds, it’s a baseline investment every forward-looking security program should plan for in 2026 and beyond.

3. Invest in Testing

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Training builds capability. Visibility builds understanding. Testing builds credibility.

External red team, purple team, or security posture assessments continue to be among the most effective ways to validate your defenses and generate actionable findings.

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Security practitioners often point out that testing engagements create outcomes leadership understands:

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“Training is great, but it’s hard to quantify. An external assessment gives you findings, metrics, and a roadmap you can point to when defending next year’s budget.”

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Well-scoped assessments do more than uncover vulnerabilities—they benchmark performance, expose process gaps, and generate data-backed justification for continued investment.

4. Preserve Flexibility with a Retainer

If your team can’t launch a new project before year-end, a retainer with a trusted partner is an efficient way to preserve funds without waste. Retainers can cover services like penetration testing, incident response, or advisory hours, providing flexibility when unpredictable needs arise. This approach, often recommended by veteran CISOs, allows teams to close their books responsibly while keeping agility for the next fiscal year.

5. Strengthen Your Foundations

Not every valuable investment requires new tools. Several practitioners emphasized the long-term returns from process improvements and collaboration-focused initiatives:

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• Threat modeling workshops that align development and security priorities.
  
• Framework assessments (like NIST CSF or ISO 27001) that provide measurable baselines.
  
• Automation pilots to eliminate repetitive manual work.
  
• Internal tabletop exercises that enhance cross-team coordination.
  
  

These lower-cost efforts improve resilience and efficiency, two metrics that always matter in budget conversations.

How to Decide: A Simple, Measurable Framework

When evaluating where to allocate remaining or future funds, apply a simple framework:

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1. Identify what’s lagging. Which pillar - people, visibility, or process most limits your current effectiveness?
   
2. Choose something measurable. Prioritize initiatives that produce clear, demonstrable outputs: reports, dashboards, certifications.
   
3. Aim for dual impact. Every investment should strengthen both your operations and your ability to justify next year’s funding.
   
   

Final Thoughts

A strong security budget isn’t just about defense, it’s about direction. Every spend tells a story about how your organization prioritizes resilience, efficiency, and visibility.

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Whether you’re closing out this year’s funds or preparing your 2026 plan, focus on investments that create both operational value and executive clarity. Because while technologies evolve and threats shift, understanding where your data is, who can access it, and how it’s protected remains the cornerstone of a mature security program.

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Or, as one practitioner summed it up: “Spend on the things that make next year’s budget conversation easier.”

DSPM fits that description perfectly.

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Data Security Posture Management (DSPM) has quickly become a must-have for organizations moving to the cloud. By discovering, classifying, and protecting sensitive data across SaaS apps and cloud services, DSPM gave security teams visibility into data risks they never knew they had before.

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But here’s the reality: most enterprises aren’t 100% cloud. Legacy file shares, private databases, and hybrid workloads still hold massive amounts of sensitive data. Without visibility into these environments, even the most advanced DSPM platforms leave critical blind spots.

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That’s why DSPM platform support is evolving - from cloud-only to truly hybrid.

The Evolution of DSPM

DSPM emerged as a response to the visibility problem created by rapid cloud adoption. As organizations moved to cloud services, SaaS applications, and collaboration platforms, sensitive data began to sprawl across environments at a pace traditional security tools couldn’t keep up with. Security teams suddenly faced oversharing, inconsistent access controls, and little clarity on where critical information actually lived.

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DSPM helped fill this gap by delivering a new level of insight into cloud data. It allowed organizations to map sensitive information across their environments, highlight risky exposures, and begin enforcing least-privilege principles at scale. For cloud-native companies, this represented a huge leap forward - finally, there was a way to keep up with constant data changes and movements, helping customers safely adopt the cloud while maintaining data security best practices and compliance and without slowing innovation.

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But for large enterprises, the model was incomplete. Decades of IT infrastructure meant that vast amounts of sensitive information still lived in legacy databases, file shares, and private cloud environments. While DSPM gave them visibility in the cloud, it left everything else in the dark.

The Blind Spot of On-Prem & Private Data

Despite rapid cloud adoption and digital transformation progress, large organizations still rely heavily on hybrid and on-prem environments, since data movement to the cloud can be a year’s long process. On-premises file shares such as NetApp ONTAP, SMB, and NTFS, alongside enterprise databases like Oracle, SQL Server, and MySQL, remain central to operations. Private cloud applications are especially common in regulated industries like healthcare, finance, and government, where compliance demands keep critical data on-premises.

To scan on premises data, many DSPM providers offer partial solutions by taking ephemeral ‘snapshots’ of that data and temporarily moving it to the cloud (either within customer environment, as Sentra does, or to the vendor cloud as some others do) for classification analysis. This can satisfy some requirements, but often is seen as a compliance risk for very sensitive or private data which must remain on-premises. What’s left are two untenable alternatives - ignoring the data which leaves serious visibility gaps or utilizing manual techniques which do not scale.

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These approaches were clearly not built for today’s security or operational requirements. Sensitive data is created and proliferates rapidly, which means it may be unclassified, unmonitored, and overexposed, but how do you even know? From a compliance and risk standpoint, DSPM without on-prem visibility is like watching only half the field, and leaving the other half open to attackers or accidental exposure.

Expanding with On-Prem Scanners

Sentra is changing the equation. With the launch of its on-premise scanners, the platform now extends beyond the cloud to hybrid and private environments, giving organizations a single pane of glass for all their data security.

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With Sentra, organizations can:

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• Discover and classify sensitive data across traditional file shares (SMB, NFS, CIFS, NTFS) and enterprise databases (Oracle, SQL Server, MySQL, MSSQL, PostgreSDL, MongoDB, MariaDB, IBM DB2, Teradata).
  
• Detects and protects critical data as it moves between on-prem and cloud environments.
  
• Apply AI-powered classification and enforce Microsoft Purview labeling consistently across environments.
  
• Strengthen compliance with frameworks that demand full visibility across hybrid estates.
  
• Have a choice of deployment models that best fits their security, compliance, and operational requirements.
  
  

Crucially, Sentra’s architecture allows customers to ensure private data always remains in their own environment. They need not move data outside their premises and nothing is ever copied into Sentra’s cloud, making it a trusted choice for enterprises that require secure, private data processing.

Extending the Hybrid Vision

This milestone builds on Sentra’s proven track record as the only cloud-native data security platform that guarantees data always remains within the customer’s cloud environments - never copied or stored in Sentra’s cloud.

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Now, Sentra’s AI-powered classification and governance engine can also be deployed in organizations that require onsite data processing, giving them the flexibility to protect both structured and unstructured data across cloud and on-premises systems.

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By unifying visibility and governance across all environments while maintaining complete data sovereignty, Sentra continues to lead the next phase of DSPM, one built for modern, hybrid enterprises.

Real-World Impact

Picture a global bank: with modern customer-facing websites and mobile applications hosted in the public cloud, providing agility and scalability for digital services. At the same time, the bank continues to rely on decades-old operational databases running in its private cloud — systems that power core banking functions such as transactions and account management. Without visibility into both, security teams can’t fully understand the risks these stores may pose and enforce least privilege, prevent oversharing, or ensure compliance.

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With hybrid DSPM powered by on-prem scanners, that same bank can unify classification and governance across every environment - cloud or on-prem, and close the gaps that attackers or AI systems could otherwise exploit.

Conclusion

DSPM solved the cloud problem. But enterprises aren’t just in the cloud, they’re hybrid. Legacy systems and private environments still hold critical data, and leaving them out of your security posture is no longer an option.

Sentra’s on-premise scanners mark the next stage of DSPM evolution: one unified platform for cloud, on-prem, and private environments. With full visibility, accurate classification, and consistent governance, enterprises finally have the end-to-end data security they need for the AI era. Because protecting half your data is no longer enough.

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In recent years, the landscape of data privacy and protection has become increasingly stringent, with regulators around the world cracking down on companies that fail to comply with local and international standards.

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The latest high-profile case involves TikTok, which was recently fined a staggering €530 million ($600 million) by the Irish Data Protection Commission (DPC) for violations related to the General Data Protection Regulation (GDPR). This is a wake up call for multinational companies.

What is GDPR?

The General Data Protection Regulation (GDPR) is a data protection law that came into effect in the EU in May 2018. Its goal is to give individuals more control over their personal data and unify data protection rules across the EU.

GDPR gives extra protection to special categories of sensitive data. Both 'controllers' (who decide how data is processed) and 'processors' (who act on their behalf) must comply. Joint controllers may share responsibility when multiple entities manage data.

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Who Does the GDPR Apply To?

GDPR applies to both EU-based and non-EU organizations that handle the data of EU residents. The regulation requires organizations to obtain clear consent for data collection and processing, and it gives individuals rights to access, correct, and delete their data. Organizations must also ensure strong data security and report any data breaches promptly.

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What Are Data Subject Access Requests (DSARs)?

One of the core rights granted to individuals under GDPR is the ability to understand and control how their personal data is used. This is made possible through Data Subject Access Requests (DSARs).

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A DSAR allows any EU resident to request access to the personal data an organization holds about them. In response, the organization must provide a comprehensive overview, including:

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• What personal data is being processed
• The purpose of processing
• Data sources and recipients
• Retention periods
• Information about automated decision-making

Organizations are required to respond to DSARs within one month, making them a time-sensitive and resource-intensive obligation, especially for companies with complex data environments.

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What Are the Penalties for Non-Compliance with GDPR?

Non-compliance with the General Data Protection Regulation (GDPR) can result in substantial penalties.

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Article 83 of the GDPR establishes the fine framework, which includes the following:

Maximum Fine: The maximum fine for GDPR non-compliance can reach up to 20 million euros, or 4% of the company’s total global turnover from the preceding fiscal year, whichever is higher.

Alternative Penalty: In certain cases, the fine may be set at 10 million euros or 2% of the annual global revenue, as outlined in Article 83(4).

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Additionally, individual EU member states have the authority to impose their own penalties for breaches not specifically addressed by Article 83, as permitted by the GDPR’s flexibility clause.

So far, the maximum fine given under GDPR was to Meta in 2023, which was fined $1.3 billion for violating GDPR laws related to data transfers. We’ll delve into the details of that case shortly.

Can Individuals Be Fined for GDPR Breaches?

While fines are typically imposed on organizations, individuals can be fined under certain circumstances. For example, if a person is self-employed and processes personal data as part of their business activities, they could be held responsible for a GDPR breach. However, UK-GDPR and EU-GDPR do not apply to data processing carried out by individuals for personal or household activities. 

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According to GDPR Chapter 1, Article 4, “any natural or legal person, public authority, agency, or body” can be held accountable for non-compliance. This means that GDPR regulations do not distinguish significantly between individuals and corporations when it comes to breaches.

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Specific scenarios where individuals within organizations may be fined include:

• Obstructing a GDPR compliance investigation.
• Providing false information to the ICO or DPA.
• Destroying or falsifying evidence or information.
• Obstructing official warrants related to GDPR or privacy laws.
• Unlawfully obtaining personal data without the data controller's permission.

The Top 3 GDPR Fines and Their Impact

1.  Meta - €1.2 Billion ($1.3 Billion), 2023 

In May 2023, Meta, the U.S. tech giant, was hit with a staggering $1.3 billion fine by an Irish court for violating GDPR regulations concerning data transfers between the E.U. and the U.S. This massive penalty came after the E.U.-U.S. Privacy Shield Framework, which previously provided legal cover for such transfers, was invalidated in 2020. The court found that the framework failed to offer sufficient protection for EU citizens against government surveillance. This fine now stands as the largest ever under GDPR, surpassing Amazon’s 2021 record.

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2. Amazon - €746 million ($781 million), 2021

Which leads us to Amazon at number 2, not bad. In 2021, Amazon Europe received the second-largest GDPR fine to date from Luxembourg’s National Commission for Data Protection (CNPD). The fine was imposed after it was determined that the online retailer was storing advertisement cookies without obtaining proper consent from its users.

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3. TikTok – €530 million ($600 million), 2025

The Irish Data Protection Commission (DPC) fined TikTok for failing to protect user data from unlawful access and for violating GDPR rules on international data transfers in May 2025. The investigation found that TikTok allowed EU users’ personal data to be accessed from China without ensuring adequate safeguards, breaching GDPR’s requirements for cross-border data protection and transparency. The DPC also cited shortcomings in how TikTok informed users about where their data was processed and who could access it. The case reinforced regulators’ focus on international data transfers and children’s privacy on social media platforms.

The Implications for Global Companies

The growing frequency of such fines sends a clear message to global companies: compliance with data protection regulations is non-negotiable. As European regulators continue to enforce GDPR rigorously, companies that fail to implement adequate data protection measures risk facing severe financial penalties and reputational harm.

In the case of Uber, the company’s failure to use appropriate mechanisms for data transfers, such as Standard Contractual Clauses, led to significant repercussions. This situation emphasizes the importance of staying current with regulatory changes, such as the introduction of the E.U.-U.S. Data Privacy Framework, and ensuring that all data transfer practices are fully compliant.

How Sentra Helps Organizations Stay Compliant with GDPR

Sentra helps organizations maintain GDPR compliance by effectively tagging data belonging to European citizens.

When EU citizens' Personally Identifiable Information (PII) is moved or stored outside of EU data centers, Sentra will detect and alert you in near real-time. Our continuous monitoring and scanning capabilities ensure that any data violations are identified and flagged promptly.

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Unlike traditional methods where data replication can obscure visibility and lead to issues during audits, Sentra provides ongoing visibility into data storage. This proactive approach significantly reduces the risk by alerting you to potential compliance issues as they arise.

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Sentra does automatic classification of localized data - specifically in this case, EU data. Below you can see an example of how we do this. 

The Rise of Compliance Violations: A Wake-up Call

The increasing number of compliance violations and the related hefty fines should serve as a wake-up call for companies worldwide. As the regulatory environment becomes more complex, it is crucial for organizations to prioritize data protection and privacy. By doing so, they can avoid costly penalties and maintain the trust of their customers and stakeholders.

Solutions such as Sentra provide a cost-effective means to ensure sensitive data always has the right posture and security controls - no matter where the data travels - and can alert on exceptions that require rapid remediation. In this way, organizations can remain regulatory compliant, avoid the steep penalties for violations, and ensure the proper, secure use of data throughout their ecosystem.

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To learn more about how Sentra's Data Security Platform can help you stay compliant, avoid GDPR penalties, and ensure the proper, secure use of data, request a demo today.

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In today’s data-driven world, organizations are dealing with more information than ever before. Data pours in from countless production systems and applications, and data analysts are tasked with making sense of it all - fast. To extract valuable insights, teams rely on powerful analytics platforms like Snowflake, Databricks, BigQuery, and Redshift. These tools make it easier to store, process, and analyze data at scale.

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But while these platforms are excellent at managing raw data, they don't solve one of the most critical challenges organizations face: understanding and securing that data.

That’s where metadata catalogs come in.

Metadata Catalogs Are Essential But They’re Not Enough

Metadata catalogs such as AWS Glue, Hive Metastore, and Apache Iceberg are designed to bring order to large-scale data ecosystems. They offer a clear inventory of datasets, making it easier for teams to understand what data exists, where it’s stored, and who is responsible for it.

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This organizational visibility is essential. With a good catalog in place, teams can collaborate more efficiently, minimize redundancy, and boost productivity by making data discoverable and accessible.

But while these tools are great for discovery, they fall short in one key area: security. They aren’t built to detect risky permissions, identify regulated data, or prevent unintended exposure. And in an era of growing privacy regulations and data breach threats, that’s a serious limitation.

Different Data Tools, Different Gaps

It’s also important to recognize that not all tools in the data stack work the same way. For example, platforms like Snowflake and BigQuery come with fully managed infrastructure, offering seamless integration between storage, compute, and analytics. Others, like Databricks or Redshift, are often layered on top of external cloud storage services like S3 or ADLS, providing more flexibility but also more complexity.

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Metadata tools have similar divides. AWS Glue is tightly integrated into the AWS ecosystem, while tools like Apache Iceberg and Hive Metastore are open and cloud-agnostic, making them suitable for diverse lakehouse architectures.

This variety introduces fragmentation, and with fragmentation comes risk. Inconsistent access policies, blind spots in data discovery, and siloed oversight can all contribute to security vulnerabilities.

The Blind Spots Metadata Can’t See

Even with a well-maintained catalog, organizations can still find themselves exposed. Metadata tells you what data exists, but it doesn’t reveal when sensitive information slips into the wrong place or becomes overexposed.

This problem is particularly severe in analytics environments. Unlike production environments, where permissions are strictly controlled, or SaaS applications, which have clear ownership and structured access models, data lakes and warehouses function differently. They are designed to collect as much information as possible, allowing analysts to freely explore and query it.

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In practice, this means data often flows in without a clear owner and frequently without strict permissions. Anyone with warehouse access, whether users or automated processes, can add information, and analysts typically have broad query rights across all data. This results in a permissive, loosely governed environment where sensitive data such as PII, financial records, or confidential business information can silently accumulate. Once present, it can be accessed by far more individuals than appropriate.

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The good news is that the remediation process doesn't require a heavy-handed approach. Often, it's not about managing complex permission models or building elaborate remediation workflows. The crucial step is the ability to continuously identify and locate sensitive data, understand its location, and then take the correct action whether that involves removal, masking, or locking it down.

How Sentra Bridges the Gap Between Data Visibility & Security

This is where Sentra comes in.

Sentra’s Data Security Posture Management (DSPM) platform is designed to complement and extend the capabilities of metadata catalogs, not just to address their limitations, but to elevate your entire data security strategy. Instead of replacing your metadata layer, Sentra works alongside it enhancing your visibility with real-time insights and powerful security controls.

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Sentra scans across modern data platforms like Snowflake, S3, BigQuery, and more. It automatically classifies and tags sensitive data, identifies potential exposure risks, and detects compliance violations as they happen.

With Sentra, your metadata becomes actionable.

From Static Maps to Live GPS

Think of your metadata catalog as a map. It shows you what’s out there and how things are connected. But a map is static. It doesn’t tell you when there’s a roadblock, a detour, or a collision. Sentra transforms that map into a live GPS. It alerts you in real time, enforces the rules of the road, and helps you navigate safely no matter how fast your data environment is moving.

Conclusion: Visibility Without Security Is a Risk You Can’t Afford

Metadata catalogs are indispensable for organizing data at scale. But visibility alone doesn’t stop a breach. It doesn’t prevent sensitive data from slipping into the wrong place, or from being accessed by the wrong people.

To truly safeguard your business, you need more than a map of your data—you need a system that continuously detects, classifies, and secures it in real time. Without this, you’re leaving blind spots wide open for attackers, compliance violations, and costly exposure.

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Sentra turns static visibility into active defense. With real-time discovery, context-rich classification, and automated protection, it gives you the confidence to not only see your data, but to secure it.

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See clearly. Understand fully. Protect confidently with Sentra.

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Introduction

We’re excited to announce that Sentra has officially achieved TX-RAMP certification, a significant milestone that underscores our commitment to delivering trusted, compliant, and secure cloud data protection.

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The Texas Risk and Authorization Management Program (TX-RAMP) establishes rigorous security standards for cloud products and services used by Texas state agencies. Achieving this certification validates that Sentra meets and exceeds these standards, ensuring our customers can confidently rely on our platform to safeguard sensitive data.

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For agencies and organizations operating in Texas, this means streamlined procurement, faster adoption, and the assurance that Sentra’s solutions are fully aligned with state-mandated compliance requirements. For our broader customer base, TX-RAMP certification reinforces Sentra’s role as a trusted leader in data security posture management (DSPM) and our ongoing dedication to protecting data everywhere it lives.

What is TX-RAMP?

The Texas Risk and Authorization Management Program (TX-RAMP) is the state’s framework for evaluating the security of cloud solutions used by public sector agencies. Its goal is to ensure that organizations working with Texas state data meet strict standards for risk management, compliance, and operational security.

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TX-RAMP certification focuses on key areas such as:

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• Audit & Accountability: Ensuring system activity is monitored, logged, and reviewed.
• System Integrity: Protecting against malicious code and emerging threats.
• Access Control: Managing user accounts and privileges with least-privilege principles.
• Policy & Governance: Establishing strong security policies and updating them regularly.
  
  

By certifying vendors, TX-RAMP helps agencies reduce risk, streamline procurement, and ensure sensitive state and citizen data is well protected.

Why TX-RAMP Certification Matters

For Texas agencies, TX-RAMP certification means trust and speed. Working with a certified partner like Sentra simplifies procurement, reduces onboarding time, and provides confidence that solutions meet the state’s toughest security requirements.

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For enterprises and organizations outside Texas, this milestone is just as meaningful. TX-RAMP certification validates that Sentra’s DSPM platform can meet and go beyond some of the most demanding compliance frameworks in the U.S. It’s another proof point that when customers choose Sentra, they are choosing a solution built with security, accountability, and transparency at its core.

Sentra’s Path to TX-RAMP Certification

Achieving TX-RAMP certification required proving that Sentra’s security controls align with strict state requirements.

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Some of the measures that demonstrate compliance include:

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• Audit and Accountability: Continuous monitoring and quarterly reviews of audit logs under SOC 2 Type II governance.
  
• System and Information Integrity: Endpoint protection and weekly scans to prevent, detect, and respond to malicious code.
  
• Access Control: Strong account management practices using Okta, BambooHR, MFA, and quarterly access reviews.
  
• Change Management and Governance: Structured SDLC processes with documented requests, multi-level approvals, and complete audit trails.
  
  

Together, these safeguards show that Sentra doesn’t just comply with TX-RAMP - we exceed the requirements, embedding security into every layer of our operations and platform.

What This Means for Sentra Customers

For Texas agencies, TX-RAMP certification makes it easier and faster to adopt Sentra’s platform, knowing that it has already been vetted against the state’s most stringent standards.

For global enterprises, it’s another layer of assurance: Sentra’s DSPM solution is designed to stand up to the highest levels of compliance practice, giving customers confidence that their most sensitive data is secure - wherever it lives.

Conclusion

Earning TX-RAMP certification is a major milestone in Sentra’s journey, but it’s only part of our broader mission: building trust through security, compliance, and innovation.

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This recognition reinforces Sentra’s role as a leader in data security posture management (DSPM) and gives both public sector and private enterprises confidence that their data is safeguarded by a platform designed for the most demanding environments.

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October is shaping up to be a big month for Sentra! From coast to coast, our team will be meeting with security leaders to share insights on securing sensitive data - no matter where it travels.

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If you’re attending one of these top cybersecurity conferences, we’d love to connect and show you how Sentra helps organizations embrace innovation while keeping data secure. Here’s where you can find us this month:

Hou.Sec.Con: September 30–October 1, Houston, TX

We’re kicking off in Texas at Hou.Sec.Con, one of the region’s most anticipated security conferences. It’s a hub for IT and cybersecurity professionals looking to explore new ways to defend against today’s evolving threats.

Stop by and learn how Sentra helps organizations protect sensitive data across cloud environments.

Trace3 Evolve: September 30–October 3, Las Vegas, NV

Next up is Trace3 Evolve, where IT leaders and innovators gather to discuss the future of enterprise technology. With cloud adoption accelerating, conversations around data security, compliance, and innovation are more important than ever.

Meet our team to see how Sentra makes securing sensitive data simple and scalable.

GuidePoint GPSEC Security Forum: October 3, Dallas, TX

We’re heading back south to attend GuidePoint GPSEC Security Forum in Dallas which will bring together industry leaders, cybersecurity experts, and technology innovators for a full day of impactful conversations, networking, and hands-on learning. This conference will dive into today’s most pressing security challenges through dynamic keynote speakers, engaging breakout sessions, and a bustling vendor fair. 

Whether you're dealing with data sprawl, compliance complexity, or risk visibility, Sentra will be on-site to show how their platform helps reduce risk and strengthen security posture without slowing innovation.

GrrCON: October 2–3, Grand Rapids, MI

Heading north, we’ll be at GrrCON, a favorite for security practitioners, researchers, and executives alike. Known for its community-driven feel, this event fosters knowledge-sharing and collaboration.

Let’s chat about modern approaches to cloud data security and how to mitigate risk without slowing innovation.

Innovate Cybersecurity Summit: October 5–7, Scottsdale, AZ

We’re excited to join the Innovate Cybersecurity Summit, where industry leaders explore solutions to today’s toughest challenges in data protection and cyber defense.

Learn how Sentra empowers organizations to gain visibility into their sensitive data and take proactive steps to secure it.

FS-ISAC Scottsdale: October (Dinner & Meetings)

We will be in Scottsdale during FS-ISAC, a premier financial services cybersecurity community event.

Sentra will be hosting a private dinner where attendees can connect in an intimate setting. We’ll also be available for 1:1 meetings to discuss how Sentra helps financial institutions protect sensitive data and comply with complex regulatory requirements.

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This is a great chance to meet our team and hear how we partner with organizations to balance innovation and data protection.

Gartner Symposium: October 20–23, Orlando, FL

One of the year’s biggest IT events, the Gartner Symposium brings together CIOs, CISOs, and technology leaders to discuss the future of digital business.

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Sentra will be on-site at Booth #748, where our team will showcase how a data-first security approach empowers organizations to innovate confidently while ensuring sensitive information remains protected. Stop by to connect with our experts and learn how Sentra helps enterprises stay secure in the cloud era.

NYC Google Event: October 21, New York, NY

We’ll also be in New York City at the Google Event, connecting with forward-thinking organizations adopting cutting-edge cloud technologies.

Discover how Sentra seamlessly integrates with Google Cloud to protect sensitive data wherever it lives.

InfoSec World: October 27–29, Lake Buena Vista, FL

We’re wrapping up the month at InfoSec World, a leading cybersecurity event bringing together professionals from across industries.

Stop by to learn how Sentra helps organizations strengthen data security strategies and stay ahead of regulatory demands.

GuidePoint GPSEC Security Forum: October 29, Philadelphia, PA

We’re closing out October at the GuidePoint GPSEC Security Forum in Philadelphia. This annual event brings together security professionals, technology partners, and thought leaders for a full day of collaboration and learning.

Hosted at Convene at Commerce Square, the forum will run from 8:00 a.m. to 5:00 p.m. ET and features a rich agenda, including:

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• A keynote from a leading cybersecurity expert
  
• Breakout sessions exploring today’s most pressing security challenges
  
• A panel of CISOs sharing practical strategies and real-world insights
  
• A showcase of more than 70 technology vendors driving innovation in security
  
  

The day wraps up with a networking reception, providing attendees with the opportunity to connect with peers, exchange ideas, and continue important conversations in a more relaxed setting. Sentra is proud to participate in this event and contribute to the dialogue on securing sensitive data in an increasingly complex landscape.

Why These Events Matter

Cybersecurity is a team sport. By joining these events, Sentra isn’t just sharing our vision for protecting sensitive data, we’re also listening, learning, and collaborating with the community to address the most pressing challenges in cloud security.

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From data discovery and classification to continuous monitoring and protection, Sentra helps organizations embrace innovation without compromising on security.

Connect with Sentra This October

Will you be at one of these events? Let’s meet!

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Schedule a meeting with Sentra or visit our team at any of the conferences listed above. We’d love to show you how we can help your organization protect sensitive data and move faster with confidence.

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See you on the road this October!

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Introduction

Cloud-first travel platforms handle massive volumes of customer data every day, from booking details to payment information. With petabytes of data spread across hundreds of  cloud accounts, the stakes couldn’t be higher: customer trust, regulatory pressure (PCI DSS, GDPR), and business reputation are always on the line.

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This is the story of how a global travel platform took action to ensure the highest level of customer data protection and set out to gain complete visibility and full control of its data estate, securing petabytes of sensitive information across 600+ cloud accounts in just 30 days.

At a Glance: Securing Petabytes at Scale

The Challenge

• 100s of PBs of sensitive customer data
  
• 600+ cloud accounts, 150K+ data stores
  
• Manual tagging, blind spots, reactive DLP
  
• Compliance risks (PCI DSS, GDPR)
  
  

The Solution

• Sentra’s agentless DSPM platform
  
• Automated discovery & AI-driven classification
  
• Real-time data mapping and compliance alignment
  
• Partnership-driven support and fast deployment
  
  

The Results

• Full visibility across petabytes of data in 30 days
  
• Streamlined governance across 600+ cloud accounts
  
• Dramatic reduction in  false positives & alert fatigue
  
• Stronger compliance with PCI DSS & GDPR
  
• Data security transformed into a strategic advantage
  
  

The Challenge: Data Visibility at Scale

The travel tech company’s cloud footprint had grown rapidly, now its security practices needed to be brought up to speed. Relying on legacy Data Loss Prevention (DLP) tools left the security team in a reactive posture. Alerts were triggered only after data had already left the environment. In the high velocity world of digital travel, “too late” is not an acceptable outcome.

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Manual tagging compounded the problem. It was slow, resource-intensive, inconsistent across teams, and prone to human error. With more than 600 cloud accounts and hundreds of petabytes of data in motion, the organization sought a reliable way to answer the most fundamental security questions:

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• What sensitive data do we have?
  
• Where is it stored?
  
• Who has access to it?

Answers to these three foundational questions would enable them to lock down exposure risk, misconfigurations, and regulatory noncompliance for sensitive customer information, including payment card data and personal identifiers.

Sentra Data Security: Scalable, Accurate, Agentless

After evaluating a wide mix of DLP and DSPM vendors, the company selected Sentra for its ability to deliver scale, accuracy, and scan efficiency.

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• Agentless discovery allowed rapid deployment across the entire multi-cloud environment without adding operational friction.
  
• AI-driven classification replaced error-prone manual tagging, enabling sensitive data to be labeled consistently and accurately.
  
• Regulatory mapping ensured risks were tied directly to frameworks such as PCI DSS and GDPR, making compliance reviews easier and faster.
  
• Smart scanning lowered cloud compute costs and provided more timely results.
  
  

Just as importantly, Sentra’s customer success and engineering teams worked closely with the company. Rapid support and the ability to deliver custom features strengthened the partnership and accelerated adoption.

Implementation: Tackling Complexity Head-On

Securing hundreds of petabytes across over 600 cloud accounts, over 150K data stores, and 25K data storage locations was no small feat. The implementation involved coordination with six internal stakeholder teams.

Sentra’s engineering team collaborated directly with the customer to fine-tune scanning for high-memory formats and optimize scanning cycles. This ensured that even as the environment expanded, sensitive data could still be discovered, classified, and secured in near real time.

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Despite the scope and complexity, deployment was completed on schedule. Within weeks, the company moved from chasing alerts to uncovering exposures proactively. Manual tagging errors were eliminated, and governance workflows became more consistent across business units.

Real Business Impact: From Reactive to Proactive

The shift in outcomes was dramatic. Within months, the security team achieved the visibility they sought. Instead of reacting to alerts, they were proactively discovering risks and preventing incidents before they escalated.

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Key results included:

• Discovery of sensitive data that had previously gone unnoticed
  
• Streamlined governance across 600+ cloud accounts
  
• Automated classification that reduced false positives and alert fatigue
  
• Improved compliance posture with PCI DSS and GDPR
  
  

As one security engineering manager put it:

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“The Sentra speed and support really stood out. We were able to quickly transform our approach from reactive alerts to proactive discovery. We’re not just detecting potential risks anymore; we’re gaining a comprehensive inventory of our data landscape across hundreds of petabytes, enabling us to truly protect our most critical assets.”

Sentra for Travel Tech: Setting the Pace

For travel technology companies, customer trust and agility are everything. Every transaction, every booking, every passenger record carries sensitive information that must be protected. At this scale, manual processes and reactive tools simply cannot keep up.

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By adopting Sentra’s cloud-native DSPM platform, this global travel leader gained real-time visibility into its vast, fast-moving data estate. Booking and flight details, payment card data, and personal identifiers could now be classified automatically and governed consistently without slowing the pace of innovation.

What had once been a compliance bottleneck became a strategic advantage.

Bottom Line: Data Security is a Competitive Edge

The journey of this global travel platform illustrates what’s possible when scale, automation, and accuracy come together. In just 30 days, the company moved from dangerous blind spots to full visibility and control over petabytes of sensitive data.

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But this is about more than one company’s success story. In the AI-powered economy, where data volumes are exploding and regulatory demands are intensifying, innovation speed without security is a liability. The leaders of the next decade will be those who can combine agility with trusted data security.

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Sentra’s DSPM platform gives organizations the ability to:

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• Discover and classify sensitive data automatically
  
• Map risks directly to compliance frameworks
  
• Move from reactive alerts to proactive governance
  
• Scale confidently across complex, cloud-first environments
  
  

This is about more than just compliance. For consumer industries like travel and hospitality, retail, financial services, and any enterprise that runs on data, it’s about protecting customer trust, unlocking innovation, and gaining a true competitive edge.

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Discover how Sentra can help your organization secure its cloud data estate at scale.

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NYDFS Steps Up Enforcement

The New York State Department of Financial Services (NYDFS) has long been one of the most influential regulators in the financial sector, but over the past two years, it’s made one thing crystal clear: cybersecurity is no longer a back-office IT concern, it’s a regulatory priority.

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In response to growing threats, increasing reliance on third-party services, and persistent operational risks, NYDFS has tightened its expectations around how financial institutions protect sensitive data. And it’s backing that stance with real financial consequences.

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Just ask PayPal or OneMain Financial, two major firms hit with multimillion-dollar penalties for cybersecurity lapses. These weren’t headline-grabbing breaches or ransomware attacks, they were the result of basic control failures, delayed reporting, and repeated gaps in governance.

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What do a $2M fine for PayPal and a $4.25M penalty for OneMain have in common?

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Weak cybersecurity practices, and a regulator that’s no longer willing to wait for companies to catch up.

The Recent Crackdowns: PayPal and OneMain

a. PayPal – $2M Civil Penalty (January 2025)

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In January 2025, NYDFS announced a $2 million penalty against PayPal for violations of its cybersecurity regulations under Part 500. The enforcement focused on failures to report a cybersecurity event in a timely manner and gaps in maintaining certain required controls.

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The incident involved unauthorized access to over 34,000 user accounts, exposing sensitive personal data including tax IDs and financial information. NYDFS emphasized that PayPal’s delayed reporting and lack of specific security measures put both consumers and the broader financial ecosystem at risk.

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What it signals: No company - not even a digital-native fintech giant is immune from enforcement. The bar is rising, and NYDFS is expecting organizations to report, respond, and remediate swiftly and transparently.

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b. OneMain Financial – $4.25M Fine (May 2023)

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In May 2023, NYDFS fined OneMain Financial $4.25 million after discovering systemic cybersecurity deficiencies, including improperly stored passwords, insufficient multi-factor authentication, and inadequate third-party risk management.

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Even more concerning: many of these issues were identified in earlier audits and hadn’t been fully addressed. NYDFS made it clear that repeated inaction wouldn’t be tolerated.

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What it signals: It’s not just about responding to one-off incidents — regulators are watching for long-term security maturity. Ongoing hygiene, policy enforcement, and consistent control testing are now table stakes.

What’s Changing: NYDFS 2.0 (Part 500 Amendments)

These enforcement actions aren’t just about past violations, they’re a preview of what’s to come.

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With the rollout of the NYDFS Second Amendment to Part 500, also known as NYDFS 2.0, covered entities, especially those classified as Class A companies are facing a new set of enforceable expectations.

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Key new requirements include:

• Annual independent audits of cybersecurity programs
  
• Mandatory multi-factor authentication (MFA) for all systems
  
• Stronger access control policies, including role-based access
  
• Board-level or senior executive oversight of cybersecurity governance
  

Full enforcement kicks in on November 1, 2025. At that point, these aren’t just checkboxes, they’re compliance requirements with real financial and reputational risk for falling short.

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The message is clear: NYDFS is no longer satisfied with written policies and best-effort intentions. It's expecting demonstrated outcomes, measurable control, and leadership accountability.

The Broader Message: Enforcement Is the New Default

NYDFS isn’t the only regulator stepping up, but it’s arguably the most proactive, and most willing to act. These recent fines signal a broader shift across the industry: compliance is no longer about having good intentions or written policies. Regulators are now focused on evidence of execution, real controls, timely reporting, and provable outcomes.

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In other words, enforcement is the new default. This shift reframes cybersecurity from a purely technical issue to a board-level governance challenge. It's not enough for IT or security teams to manage risk in isolation. Executive leadership, legal, and compliance functions all need to be aligned — and accountable.

If your organization is treating cybersecurity as just a tech responsibility, you’re behind.

What Organizations Should Do Now

The message from regulators is clear, and now is the time to act.

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Here are four practical steps your team can take to stay ahead:

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• Audit your current posture against NYDFS Part 500. Focus especially on:
  
  • Incident reporting timelines
    
  • MFA coverage
    
  • Access controls
    
  • Third-party risk assessments
    
    
• Prioritize visibility across your environment
  You can’t protect what you can’t see. Ensure you have continuous insight into where sensitive data lives, who can access it, and how it moves across cloud, SaaS, and on-prem systems.
  
  
• Document everything
  Have clear records of your policies, security controls, vendor assessments, incident response processes, and risk decisions. If you had to prove your compliance tomorrow, could you?
  
  
• Benchmark your controls against recent enforcement
  If PayPal and OneMain were fined for these issues, ask yourself:
  ‍How would our program hold up under similar scrutiny?
  
  

Final Thoughts: Read the Signals Now, Not After a Fine

The writing is on the wall - NYDFS is raising the bar, and other regulators are likely to follow. This is your opportunity to get ahead of the curve, rather than scrambling after the fact.

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Take these fines as what they are: a warning shot and a roadmap. Organizations that prepare now - with tighter controls, better visibility, and cross-functional ownership won’t just avoid penalties. They’ll be more resilient, more trusted, and better equipped to lead in a high-risk landscape.

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If you’re not sure where to start, use these enforcement cases as a prompt for an internal review. And if you want to go deeper, we’ve put together a compliance checklist that can help you assess where you stand.

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Better to find the gaps now before NYDFS does.

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The evolving digital landscape for cloud-first companies presents unprecedented challenges for chief information security officers (CISOs). The rapid adoption of AI-powered systems and the explosive growth of cloud-based deployments have expanded the attack surface, introducing novel risks and threats.

According to IBM's 2024 "Cost of a Data Breach Report," the average cost of a cloud data breach soared to $4.88 million - prompting a crucial question: Is your organization prepared to secure its expanding digital footprint? 

Regulatory frameworks and data privacy standards are in a constant state of flux, requiring CISOs to stay agile and proactive in their approach to compliance and risk management.

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This article explores the top challenges facing CISOs today, illustrated by real-world incidents, and offers actionable solutions for them. By understanding these pressing concerns, organizations can stay proactive and secure their environments effectively.

Top Modern Challenges Faced by CISOs

Modern CISO concerns stem from a combination of technical complexity, workforce behavior, and external threats. Below, we explore these challenges in detail.

1. AI and Large Language Model (LLM) Data Protection Challenges

AI tools like large language models (LLMs) have become integral to modern organizations; however, they have also introduced significant risks to data security. In 2024, for example, Microsoft's AI system, Copilot, was manipulated to exfiltrate private data and automate spear-phishing attacks, revealing vulnerabilities in AI-powered systems.

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Furthermore, insider threats have increased as employees misuse AI tools to leak sensitive data. For instance, the AI malware Imprompter exploited LLMs to facilitate data exfiltration, causing data loss and reputational harm. 

Robust governance frameworks that restrict unauthorized AI system access and implementation of real-time activity monitoring are essential to mitigate such risks.

2. Unstructured Data Management

Unstructured data (e.g., text, images, audio, and video files) is increasingly stored across cloud platforms, making it difficult to secure. Take the high-profile breach in 2022 involving Turkish Pegasus Airlines. It compromised 6.5 TB of unstructured data stored in an AWS S3 bucket, ultimately leading to 23 million files being exposed. 

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This incident highlighted the dangers of poorly managed unstructured data, which can lead to severe reputational damage and potential regulatory penalties. Addressing this challenge requires automated classification and encryption tools to secure data at scale. In addition, real-time classification and encryption ensure sensitive information remains protected in diverse, dynamic environments.

3. Encryption and Data Labeling

Encryption and data labeling are vital for protecting sensitive information, yet many organizations struggle to implement them effectively. 

IBM's 2024 “Cost of a Data Breach Report” reveals that companies that have implemented security AI and automation “extensively” have saved an average of $2.2 million compared to those without these technologies.

The EU’s General Data Protection Regulation (GDPR) highlights the importance of data labeling and classification, requiring organizations to handle personal data appropriately based on its sensitivity. These measures are essential for protecting sensitive information and complying with all relevant data protection regulations.

Companies can enforce data protection policies more effectively by adopting dynamic encryption technologies and leveraging platforms that support automated labeling.

4. Regulatory Compliance and Global Standards

The expanding intricacies of data privacy regulations, such as GDPR, CCPA, and HIPAA, pose significant challenges for CISOs. In 2024, Microsoft and Google faced lawsuits for the unauthorized use of personal data in AI training, underscoring the financial and reputational risks of non-compliance.

Companies must leverage compliance automation tools and centralized management systems to navigate these complexities and streamline regulatory adherence.

5. Explosive Data Growth

The exponential growth of data creates immense opportunities but also heightens security risks. 

As organizations generate and store more data, legacy security measures often fall short, exposing critical vulnerabilities. Advanced, cloud-native, and scalable platforms help organizations scale their data protection strategies alongside data growth, offering real-time monitoring and automated controls to mitigate risks effectively.

6. Insider Threats

Both intentional and accidental insider threats remain among the most difficult challenges for CISOs to address. 

In 2024, a North Korean IT worker, hired unknowingly by an American company, stole sensitive data and demanded a cryptocurrency ransom. This incident exposed vulnerabilities in remote hiring processes, resulting in severe operational and reputational consequences. 

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Combatting insider threats requires sophisticated behavior analytics and activity monitoring tools to detect and respond to anomalies early. Security platforms should provide enhanced visibility into user activity, enabling organizations to mitigate such risks and secure their data proactively.

7. Shadow Data

In the race to adopt new cloud and AI-powered tools, users are often generating, storing, and transmitting sensitive data in services that the security team never approved or even knew existed. This includes everything from unofficial file-sharing apps to unsanctioned SaaS platforms and ad hoc API integrations.

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The result is shadow IT, shadow SaaS, and ultimately, shadow data: sensitive or regulated information that lives outside the visibility of traditional security tools. Without knowing where this data resides or how it’s being accessed, CISOs cannot protect it. These unknown data flows introduce real compliance, privacy, and security risk.

It is critical to expose and classify this hidden data in real time, in order to give security teams the visibility they need to secure what was previously invisible.

Overcoming the Challenges: A CISO's Playbook in 6 Steps

CISOs can follow a structured, data-driven, step-by-step playbook to navigate the hurdles of modern cybersecurity and data protection. However, in today's dynamic data landscape, simply checking off boxes is no longer sufficient—leaders must understand how each critical data security measure interconnects, creating a unified, forward-thinking strategy.

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Before diving into these steps, it's important to note why they matter now more than ever: Emerging data technologies, rapidly evolving data regulations, and escalating insider threats demand an adaptable, holistic, and data-centric approach to security. By integrating these core elements with robust data analytics, CISOs can build an ecosystem that addresses current vulnerabilities and anticipates future data risks.

1. First, Develop a Scalable Security Strategy 

A strategic security roadmap should integrate seamlessly with organizational goals and data governance frameworks, guaranteeing that risk management, data integrity, and business priorities align. 

Accurately classifying and continuously monitoring data assets, even as they move throughout the organization, is a must to achieve sustainable scale. This solid data foundation empowers organizations to quickly pivot in response to emerging threats, keeping them agile and resilient.

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The next step is key, as the right mindset is a must.

2. Build a Security-First Culture

Equip employees with the knowledge and tools to secure data effectively; regular data-focused training sessions and awareness initiatives help reduce human error and mitigate insider threats before they become critical risks. By fostering a culture of shared data responsibility, CISOs transform every team member into a first line of defense. 

This approach ensures that everyone is on the same page toward prioritizing data security. 

3. Leverage Advanced Tools and Automation

Utilize state-of-the-art platforms for comprehensive data discovery, real-time monitoring, automation, and visibility. By automating routine security tasks and delivering instant data-driven insights, these features empower CISOs to stay on top of new threats and make decisions based on the latest data. 

Naturally, even the best tools and automation require a strategic, data-centric approach to yield optimal results.

4. Implement Zero-Trust Principles 

Implement a zero-trust approach that verifies every user, device, and data transaction, ensuring zero implicit trust within the environment. Understand who has access to what data, and implement least privilege access. Continuous identity and device validation boosts security for both external and internal threats. 

Positioning zero trust as a core principle tightens data access controls across the entire ecosystem, but organizations must remain vigilant to the most recent threats.

5. Evaluate and Update Cybersecurity Frameworks

Regularly assess security policies, procedures, and data management tools to ensure alignment with the latest trends and regulatory requirements. Keep a current data inventory, and monitor all changes. Ongoing reviews maintain relevance and effectiveness, preventing outdated defenses from becoming liabilities.

For optimal data security, cross-functional collaboration is key.

6. Encourage Cross-Departmental Collaboration

Work closely with other teams, including IT, legal, compliance, and data governance, to ensure a unified and practical approach to data security challenges. Cooperation among stakeholders accelerates decision-making, streamlines incident response, and underscores the importance of security as a shared enterprise objective.

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By adopting this data-centric playbook, CISOs can strengthen their organization's security posture, respond to threats quickly, and reduce the likelihood and impact of breaches. Platforms such as Sentra provide robust, data-driven tools and capabilities to execute this strategy effectively, enabling CISOs to confidently handle complex cybersecurity landscapes.  When these steps intertwine, the result is a robust defense that adapts to the ever-shifting digital landscape - empowering leaders to stay one step ahead.

The Sentra Edge

Sentra is an advanced data security platform that offers the strategic insights and automated capabilities modern CISOs need to navigate evolving threats without compromising agility or compliance. Sentra integrates seamlessly with existing processes, empowering security leaders to build holistic programs that anticipate new risks, reinforce best practices, and protect data in real time.

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Below are several key areas where Sentra's approach aligns with the thought leadership necessary to stay ahead of modern cybersecurity challenges.

Secure Structured Data

Structured data - in tables, databases, and other organized repositories, forms the backbone of an organization’s critical assets. At Sentra, we prioritize structured data management first and foremost, ensuring automation drives our security strategy. While securing structured data might seem straightforward, rapid data proliferation can quickly overwhelm manual safeguards, exposing your data. By automating data movement tracking, continuous risk and security posture assessments, and real-time alerts for policy violations, organizations can offload these burdensome yet essential tasks. 

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This automation-first approach not only strengthens data security but also ensures compliance and operational efficiency in today’s fast-paced digital landscape.

Secure Unstructured Data

Securing text, images, video, and other unstructured data is often challenging in cloud environments. Unstructured data is particularly vulnerable when organizations lack automated classification and encryption, creating blind spots that bad actors can exploit.

In response, Sentra underscores the importance of continuous data discovery, labeling, and protection—enabling CISOs to maintain visibility over their dynamic cloud assets and reduce the risk of inadvertent exposure.

Navigate Complex Regulations

Modern data protection laws, such as GDPR and CCPA, demand rigorous compliance structures that can strain security teams. Sentra's approach highlights centralized governance and real-time reporting, helping CISOs align with ever-shifting global standards.

By automating repetitive compliance tasks, organizations can focus more energy on strategic security initiatives, ensuring they remain nimble even as regulations evolve.

Tackle Insider Threats

Insider threats—accidental and malicious—remain one of the most challenging hurdles for CISOs. Sentra advocates a multi-layered strategy that combines behavior analytics, anomaly detection, and dynamic data labeling; this offers proactive visibility into user actions, enabling security leaders to detect and neutralize insider risks early. 

Such a holistic posture helps mitigate breaches before they escalate and preserves organizational trust.

Be Prepared for Future Risks

AI-driven attacks and large language model (LLM) vulnerabilities are no longer theoretical—they are rapidly emerging threats that demand forward-thinking responses. Sentra's focus on robust data control mechanisms and continuous monitoring means CISOs have the tools they need to safeguard sensitive information, whether it's accessed by human users or AI systems. 

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This outlook helps security teams adapt quickly to the next wave of challenges. By emphasizing strategic insights, proactive measures, and ongoing adaptation, Sentra exemplifies an industry-leading approach that empowers CISOs to navigate complex data security landscapes without losing sight of broader organizational objectives.

Conclusion

As new threat vectors emerge and organizations face mounting pressures to protect their data, the role of CISO will become even more critical. Addressing modern challenges requires a proactive and strategic approach, incorporating robust security frameworks, cutting-edge tools, and a culture of vigilance.

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Sentra's platform is a comprehensive data security solution designed to empower CISOs with the tools they need to navigate this complex landscape. By addressing key hurdles such as AI risks, structured and unstructured data management, and compliance, Sentra enables companies to stay on top of evolving risks and safeguard their operations. The modern CISO role is more demanding than ever, but the right tools make all the difference. Discover how Sentra's cloud-native approach empowers you to conquer pressing security challenges.

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Microsoft 365 Copilot is a game-changer, embedding generative AI directly into your favorite tools like Word, Outlook, and Teams, and giving productivity a huge boost. But for governance, risk, and compliance (GRC) officers and CISOs, this exciting new innovation also brings new questions about governing sensitive data.

So, how can your organization truly harness Copilot safely without risking compliance? What are Microsoft 365 Copilot security best practices?

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Frameworks like NIST’s AI Risk Management and the EU AI Act offer broad guidance, but they don't prescribe exact controls. At Sentra, we recommend a practical approach: treat Copilot as a sensitive data store capable of serving up data (including highly sensitive, regulated information).

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This means applying rigorous data security measures to maintain compliance. Specifically, you'll need to know precisely what data Copilot can access, secure it, clearly map access, and continuously monitor your overall data security posture.

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We tackle Copilot security through two critical DSPM concepts: Sanitization and Governance.

1. Sanitization: Minimize Unnecessary Data Exposure

Think of Copilot as an incredibly powerful search engine. It can potentially surface sensitive data hidden across countless repositories. To prevent unintended leaks, your crucial first step is to minimize the amount of sensitive data Copilot can access.

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Address Shadow Data and Oversharing

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It's common for organizations to have sensitive data lurking in overlooked locations or within overshared files. Copilot's incredible search capabilities can suddenly bring these vulnerabilities to light. Imagine a confidential HR spreadsheet, accidentally shared too broadly, now easily summarized by Copilot for anyone who asks.

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The solution? Conduct thorough data housekeeping. This means identifying, archiving, or deleting redundant, outdated, or improperly shared information. Crucially, enforce least privilege access by actively auditing and tightening permissions – ensuring only essential identities have access to sensitive content.

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How Sentra Helps

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Sentra's DSPM solution leverages advanced AI technologies (like OCR, NER, and embeddings) to automatically discover and classify sensitive data across your entire Microsoft 365 environment. Our intuitive dashboards quickly highlight redundant files, shadow data, and overexposed folders. What's more, we meticulously map access at the identity level, clearly showing which users can access what specific sensitive data – enabling rapid remediation.

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For example, in the screenshot below, you'll see a detailed view of an identity (Jacob Simmons) within our system. This includes a concise summary of the sensitive data classes they can access, alongside a complete list of accessible data stores and data assets.

2. Governance: Control AI Output to Prevent Data Leakage

Even after thorough sanitization, some sensitive data must remain accessible within your environment. This is where robust governance comes in, ensuring that Copilot's output never becomes an unintentional vehicle for sensitive data leakage.

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Why Output Governance Matters

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Without proper controls, Copilot could inadvertently include sensitive details in its generated content or responses. This risk could lead to unauthorized sharing, unchecked sensitive data sprawl, or severe regulatory breaches. The recent EchoLeak vulnerability, for instance, starkly demonstrated how attackers might exploit AI-generated outputs to silently leak critical information.

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Leveraging DLP and Sensitivity Labels

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Microsoft 365’s Purview Information Protection and DLP policies are powerful tools that allow organizations to control what Copilot can output. Properly labeled sensitive data, such as documents marked “Confidential – Financial,” prompt Copilot to restrict content output, providing users only with references or links rather than sensitive details.

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Sentra’s Governance Capabilities

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Sentra automatically classifies your data and intelligently applies MPIP sensitivity labels, directly powering Copilot’s critical DLP policies. Our platform integrates seamlessly with Microsoft Purview, ensuring sensitive files are accurately labeled based on flexible, custom business logic. This guarantees that Copilot's outputs remain fully compliant with your active DLP policies.

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Below is an example of Sentra’s MPIP label automation in action, showing how we place sensitivity labels on data assets that contain Facebook profile URLs and credit card numbers belonging to EU citizens, which were modified in the past year:

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Additionally, our continuous monitoring and real-time alerts empower organizations to immediately address policy violations – for instance, sensitive data with missing or incorrect MPIP labels – helping you maintain audit readiness and seamless compliance alignment.

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A Data-Centric Security Approach to AI Adoption

By strategically combining robust sanitization and strong governance, you ensure your regulated data remains secure while enabling safe and compliant Copilot adoption across your organization. This approach aligns directly with the core principles outlined by NIST and the EU AI Act, effectively translating high-level compliance guidance into actionable, practical controls.

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At Sentra, our mission is clear: to empower secure AI innovation through comprehensive data visibility and truly automated compliance. Our cutting-edge solutions provide the transparency and granular control you need to confidently embrace Copilot’s powerful capabilities, all without risking costly compliance violations.

Next Steps

Adopting Microsoft 365 Copilot securely doesn’t have to be complicated. By leveraging Sentra’s comprehensive DSPM solutions, your organization can create a secure environment where Copilot can safely enhance productivity without ever exposing your regulated data.

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Ready to take control? Contact a Sentra expert today to learn more about seamlessly securing your sensitive data and confidently deploying Microsoft 365 Copilot.

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Scaling Automated Data Security in Cloud and AI Environments

Modern cloud and AI environments move faster than human response. By the time a manual workflow catches up, sensitive data may already be at risk. Organizations need automated remediation to reduce response time, enforce policy at scale, and safeguard sensitive data the moment it becomes exposed. Comprehensive data discovery and accurate data classification are foundational to this effort. Without knowing what data exists and how it's handled, automation can't succeed.

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Sentra’s cloud-native Data Security Platform (DSP) delivers precisely that. With built-in, context-aware automation, data discovery, and classification, Sentra empowers security teams to shift from reactive alerting to proactive defense. From discovery to remediation, every step is designed for precision, speed, and seamless integration into your existing security stack. precisely that. With built-in, context-aware automation, Sentra empowers security teams to shift from reactive alerting to proactive defense. From discovery to remediation, every step is designed for precision, speed, and seamless integration into your existing security stack.

Automated Remediation: Turning Data Risk Into Action

Sentra doesn't just detect risk, it acts. At the core of its value is its ability to execute automated remediation through native integrations and a powerful API-first architecture. This lets organizations immediately address data risks without waiting for manual intervention.

Key Use Cases for Automated Data Remediation

Sensitive Data Tagging & Classification Automation

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Sentra accurately classifies and tags sensitive data across environments like Microsoft 365, Amazon S3, Azure, and Google Cloud Platform. Its Automation Rules Page enables dynamic labels based on data type and context, empowering downstream tools to apply precise protections.

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‍Automated Access Revocation & Insider Risk Mitigation

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Sentra identifies excessive or inappropriate access and revokes it in real time. With integrations into IAM and CNAPP tools, it enforces least-privilege access. Advanced use cases include Just-In-Time (JIT) access via SOAR tools like Tines or Torq.

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Enforced Data Encryption & Masking Automation

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Sentra ensures sensitive data is encrypted and masked through integrations with Microsoft Purview, Snowflake DDM, and others. It can remediate misclassified or exposed data and apply the appropriate controls, reducing exposure and improving compliance.

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Integrated Remediation Workflow Automation

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Sentra streamlines incident response by triggering alerts and tickets in ServiceNow, Jira, and Splunk. Context-rich events accelerate triage and support policy-driven automated remediation workflows.

Architecture Built for Scalable Security Automation

Cloud & AI Data Visibility with Actionable Remediation

Sentra provides visibility across AWS, Azure, GCP, and M365 while minimizing data movement. It surfaces actionable guidance, such as missing logging or improper configurations, for immediate remediation.

Dynamic Policy Enforcement via Tagging

Sentra’s tagging flows directly into cloud-native services and DLP platforms, powering dynamic, context-aware policy enforcement.

API-First Architecture for Security Automation

With a REST API-first design, Sentra integrates seamlessly with security stacks and enables full customization of workflows, dashboards, and automation pipelines.

Why Sentra for Automated Remediation?

Sentra offers a unified platform for security teams that need visibility, precision, and automation at scale. Its advantages include:

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• No agents or connectors required
• High-accuracy data classification for confident automation
• Deep integration with leading security and IT platforms
• Context-rich tagging to drive intelligent enforcement
• Built-in data discovery that powers proactive policy decisions
• OpenAPI interface for tailored remediation workflows

These capabilities are particularly valuable for CISOs, Heads of Data Security, and AI Security teams tasked with securing sensitive data in complex, distributed environments. 

Automate Data Remediation and Strengthen Cloud Security

Today’s cloud and AI environments demand more than visibility, they require decisive, automated action. Security leaders can no longer afford to rely on manual processes when sensitive data is constantly in motion.

Sentra delivers the speed, precision, and context required to protect what matters most. By embedding automated remediation into core security workflows, organizations can eliminate blind spots, respond instantly to risk, and ensure compliance at scale.

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As we move deeper into 2025, the cybersecurity landscape is experiencing a profound shift. AI-driven threats are becoming more sophisticated, cloud misconfigurations remain a persistent risk, and data breaches continue to grow in scale and cost.

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In this rapidly evolving environment, traditional security approaches are no longer enough. At Black Hat USA 2025, Sentra will demonstrate how security teams can stay ahead of the curve through data-centric strategies that focus on visibility, risk reduction, and real-time response. Join us on August 4-8 at the Mandalay Bay Convention Center in Las Vegas to learn how Sentra’s platform is reshaping the future of cloud data security.

Understanding the Stakes: 2024’s Security Trends

Recent industry data underscores the urgency facing security leaders. Ransomware accounted for 35% of all cyberattacks in 2024 - an 84% increase over the prior year. Misconfigurations continue to be a leading cause of cloud incidents, contributing to nearly a quarter of security events. Phishing remains the most common vector for credential theft, and the use of AI by attackers has moved from experimental to mainstream.

These trends point to a critical shift: attackers are no longer just targeting infrastructure or endpoints. They are going straight for the data.

Why Data-Centric Security Must Be the Focus in 2025

The acceleration of multi-cloud adoption has introduced significant complexity. Sensitive data now resides across AWS, Azure, GCP, and SaaS platforms like Snowflake and Databricks. However, most organizations still struggle with foundational visibility - not knowing where all their sensitive data lives, who has access to it, or how it is being used.

Sentra’s approach to Data Security Posture Management (DSPM) is built to solve this problem. Our platform enables security teams to continuously discover, identify, classify, and secure sensitive data across their cloud environments, and to do so in real time, without agents or manual tagging.

Sentra at Black Hat USA 2025: What to Expect

At this year’s conference, Sentra will be showcasing how our DSPM and Data Detection and Response (DDR) capabilities help organizations proactively defend their data against evolving threats. Our live demonstrations will highlight how we uncover shadow data across hybrid and multi-cloud environments, detect abnormal access patterns indicating insider threats, and automate compliance mapping for frameworks such as GDPR, HIPAA, PCI-DSS, and SOX. Attendees will also gain visibility into how our platform enables data-aware threat detection that goes beyond traditional SIEM tools.

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In addition to product walkthroughs, we’ll be sharing real-world success stories from our customers - including a fintech company that reduced its cloud data risk by 60% in under a month, and a global healthtech provider that cut its audit prep time from three weeks to just two days using Sentra’s automated controls.

Exclusive Experiences for Security Leaders

Beyond the show floor, Sentra will be hosting a VIP Security Leaders Dinner on August 5 - an invitation-only evening of strategic conversations with CISOs, security architects, and data governance leaders. The event will feature roundtable discussions on 2025’s biggest cloud data security challenges and emerging best practices.

For those looking for deeper engagement, we’re also offering one-on-one strategy sessions with our experts. These personalized consultations will focus on helping security leaders evaluate their current DSPM posture, identify key areas of risk, and map out a tailored approach to implementing Sentra’s platform within their environment.

Why Security Teams Choose Sentra

Sentra has emerged as a trusted partner for organizations tackling the challenges of modern data security. We were named a "Customers’ Choice" in the Gartner Peer Insights Voice of the Customer report for DSPM, with a 98% recommendation rate and an average rating of 4.9 out of 5. GigaOm also recognized Sentra as a Leader in its 2024 Radar reports for both DSPM and Data Security Platforms.

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More importantly, Sentra is helping real organizations address the realities of cloud-native risk. As security perimeters dissolve and sensitive data becomes more distributed, our platform provides the context, automation, and visibility needed to protect it.

Meet Sentra at Booth 4408

Black Hat USA 2025 offers a critical opportunity for security leaders to re-evaluate their strategies in the face of AI-powered attacks, rising cloud complexity, and increasing regulatory pressure. Whether you are just starting to explore DSPM or are looking to enhance your existing security investments, Sentra’s team will be available for live demos, expert guidance, and strategic insights throughout the event.

Visit us at Booth 4408 to see firsthand how Sentra can help your organization secure what matters most - your data.

Register or Book a Session

• Request an invite to our VIP Dinner (August 5)
• Schedule a private strategy session with our experts

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A Women‑First Safety Tool - and a Very Public Breach

Tea is billed as a “women‑only” community where users can swap tips, background‑check potential dates, and set red or green flags. In late July 2025 the app rocketed to No. 1 in Apple’s free‑apps chart, boasting roughly four million users and a 900 k‑person wait‑list.

On 25 July 2025 a post on 4chan revealed that anyone could download an open Google Firebase Storage bucket holding verification selfies and ID photos. Technology reporters quickly confirmed the issue and confirmed the bucket had no authentication or even listing restrictions.

What Was Exposed?

About 72,000 images were taken. Roughly 13,000 were verification selfies that included “driver's license or passport photos; the rest - about 59,000 - were images, comments, and DM attachments from more than two years ago. No phone numbers or email addresses were included, but the IDs and face photos are now mirrored on torrent sites, according to public reports.

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Tea’s Official Response

On 27 July Tea posted the following notice to its Instagram account:

We discovered unauthorized access to an archived data system. If you signed up for Tea after February 2024, all your data is secure.

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This archived system stored about 72,000 user‑submitted images – including approximately 13,000 selfies and selfies that include photo identification submitted during account verification. These photos can in no way be linked to posts within Tea.

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Additionally, 59,000 images publicly viewable in the app from posts, comments, and direct messages from over two years ago were accessed. This data was stored to meet law‑enforcement standards around cyberbullying prevention.

We’ve acted fast and we’re working with trusted cyber‑security experts. We’re taking every step to protect this community – now and always.

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(Full statement: instagram.com/theteapartygirls)

How Did This Happen?

At the heart of the breach was a single, deceptively simple mistake: the Firebase bucket that stored user images had been left wide open to the internet and even allowed directory‑listing. Whoever set it up apparently assumed that the object paths were obscure enough to stay hidden, but obscurity is never security. Once one curious 4chan user stumbled on the bucket, it took only minutes to write a script that walked the entire directory tree and downloaded everything. The files were zipped, uploaded to torrent trackers, and instantly became impossible to contain. In other words, a configuration left on its insecure default setting turned a women‑safety tool into a privacy disaster.

What Developers and Security Teams Can Learn

For engineering teams, the lesson is straightforward: always start from “private” and add access intentionally. Google Cloud Storage supports Signed URLs and Firebase Auth rules precisely so you can serve content without throwing the doors wide open; using those controls should be the norm, not the exception. Meanwhile, security leaders need to accept that misconfigurations are inevitable and build continuous monitoring around them.

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Modern Data Security Posture Management (DSPM) platforms watch for sensitive data, like face photos and ID cards, showing up in publicly readable locations and alert the moment they do. Finally, remember that forgotten backups or “archive” buckets often outlive their creators’ attention; schedule regular audits so yesterday’s quick fix doesn’t become tomorrow’s headline.

How Sentra Would Have Caught This

Had Tea’s infrastructure been monitored by a DSPM solution like Sentra, the open bucket would have triggered an alert long before a stranger found it. Sentra continuously inventories every storage location in your cloud accounts, classifies the data inside so it knows those JPEGs contain faces and government IDs, and correlates that sensitivity with each bucket’s exposure. The moment a bucket flips to public‑read - or worse, gains listing permissions - Sentra raises a high‑severity alert or can even automate a rollback of the risky setting. In short, it spots the danger during development or staging, before the first user uploads a selfie, let alone before a leak hits 4chan. And, in case of a breach (perhaps by an inadvertent insider), Sentra monitors data accesses and movement and can alert when unusual activity occurs.

The Bottom Line

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One unchecked permission wiped out the core promise of an app built to keep women safe. This wasn’t some sophisticated breach, it was a default setting left in place, a public bucket no one thought to lock down. A fix that would’ve taken seconds ended up compromising thousands of IDs and faces, now mirrored across the internet.

Security isn’t just about good intentions. Least-privilege storage, signed URLs, automated classification, and regular audits aren’t extras - they’re the baseline. If you’re handling sensitive data and not doing these things, you’re gambling with trust. Eventually, someone will notice. And they won’t be the only ones downloading.

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A vulnerability like this doesn’t just compromise infrastructure, it compromises trust. When attackers gain unauthenticated access to SharePoint, they’re not just landing on a server. They’re landing on contracts, financials, customer records, and source code - the very data that defines your business.

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The latest zero-day targeting Microsoft SharePoint is a prime example. It’s not only critical in severity - it’s being actively exploited in the wild, giving threat actors a direct path to your most sensitive data.

Here’s what we know so far.

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What Happened in the Sharepoint Zero-Day Attack?

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On July 20, 2025, CISA confirmed that attackers are actively exploiting CVE-2025-53770, a remote-code-execution (RCE) zero-day that affects on-premises Microsoft SharePoint servers.

The flaw is unauthenticated and rated CVSS 9.8, letting threat actors run arbitrary code and access every file on the server - no credentials required.

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Security researchers have tied the exploits to the “ToolShell” attack chain, which steals SharePoint machine keys and forges trusted ViewState payloads, making lateral movement and persistence dangerously easy.

Microsoft has issued temporary guidance (enabling AMSI, deploying Defender AV, or isolating servers) while it rushes a full patch. Meanwhile, CISA has added CVE-2025-53770 to its Known Exploited Vulnerabilities (KEV) catalog and urges immediate mitigations. CISA

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Why Exploitation Is Alarmingly Easy

Attackers don’t need stolen credentials, phishing emails, or sophisticated malware. A typical adversary can move from a list of targets to full SharePoint server control in four quick moves:

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1. Harvest likely targets in bulk
   Public scanners like Censys, Shodan, and certificate transparency logs reveal thousands of company domains exposing SharePoint over HTTPS. A few basic queries surface sharepoint. subdomains or endpoints responding with the SharePoint logo or X-SharePointHealthScore header.
   
   
2. Check for a SharePoint host
   If a domain like sharepoint.example.com shows the classic SharePoint sign-in page, it’s likely running ASP.NET and listening on TCP 443—indicating a viable target.
   
   
3. Probe the vulnerable endpoint
   A simple GET request to /_layouts/15/ToolPane.aspx?DisplayMode=Edit should return HTTP 200 OK (instead of redirecting to login) on unpatched servers. This confirms exposure to the ToolShell exploit chain.
   
   
4. Send one unauthenticated POST
   The vulnerability lies in how SharePoint deserializes __VIEWSTATE data. With a single forged POST request, the attacker gains full RCE—no login, no MFA, no further interaction.

That’s it. From scan to shell can take under five minutes, which is why CISA urged admins to disconnect public-facing servers until patched.

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Why Data Security Leaders Should Care

SharePoint is where contracts, customer records, and board decks live. An RCE on the platform is a direct path to your crown jewel data:

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• Unbounded blast radius: Compromised machine keys let attackers impersonate any user and exfiltrate sensitive files at scale.
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• Shadow exposure: Even if you patch tomorrow, every document the attacker touched today is already outside your control.
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• Compliance risk: GDPR, HIPAA, SOX, and new AI-safety rules all require provable evidence of what data was accessed and when.

While vulnerability scanners stop at “patch fast,” data security teams need more visibility into what was exposed, how sensitive it was, and how to contain the fallout. That’s exactly what Sentra’s Data Security Posture Management (DSPM) platform delivers.

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How Sentra DSPM Neutralizes the Impact of CVE-2025-53770

• Continuous data discovery & classification: Sentra’s agentless scanner pinpoints every sensitive file - PII, PHI, intellectual-property, even AI model weights - across on-prem SharePoint, SharePoint Online, Teams, and OneDrive. No blind spots.
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• Posture-driven risk mapping: Sentra pinpoints sensitive data sitting on exploitable servers, open to the public, or granted excessive permissions, then automatically routes actionable alerts into your security team’s existing workflow platform.
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• Real-time threat detection: Sentra’s Data Detection and Response (DDR) instantly flags unusual access patterns to sensitive data, enabling your team to intervene before risk turns into breach.
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• Blast-radius analysis: Sentra shows which regulated data could have been accessed during the exploit window - crucial for incident response and breach notifications.
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• Automated workflows: Sentra integrates with Defender, Microsoft Purview, Splunk, CrowdStrike, and all leading SOARs to quarantine docs, rotate machine keys, or trigger legal hold—no manual steps required.
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• Attacker-resilience scoring: Executive dashboards translate SharePoint misconfigurations into dollar-value risk reduction and compliance posture—perfect for board updates after high-profile CVEs.

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What This Means for Your Security Team

CVE-2025-53770 won’t be the last time attackers weaponize a collaboration platform you rely on every day. With Sentra DSPM, you know exactly where your sensitive data is, how exposed it is, and how to shrink that exposure continuously.

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With Sentra DSPM, you gain more than visibility. You get the ability to map your most sensitive data, detect threats in real time, and respond with confidence - all while proving compliance and minimizing business impact.

It’s not just about patching faster. It’s about defending what matters most: your data.

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Get our checklist on how Sentra DSPM helps neutralize SharePoint zero-day risks and protects your most critical data before the next exploit hits.

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In today's digital world, data security in the cloud is essential. You rely on popular observability tools to track availability, performance, and usage—tools that keep your systems running smoothly. However, as your data flows continuously between systems and regions, you need a layer of security that delivers granular insights without disrupting performance.

Cloud service platforms provide the agility and efficiency you expect; however, they often lack the ability to monitor real-time data movement, access, and risk across diverse environments. 

This blog post explains how cloud data monitoring strategies protect your data while addressing issues like data sprawl, data proliferation, and unstructured data challenges. Along the way, we will share practical information to help you deepen your understanding and strengthen your overall security posture.

Why Real-Time Cloud Monitoring Matters

In the cloud, data does not remain static. It shifts between environments, services, and geographical locations. As you manage these flows, a critical question arises: "Where is my sensitive cloud data stored?" 

Knowing the exact location of your data in real-time is crucial for mitigating unauthorized access, preventing compliance issues, and effectively addressing data sprawl and proliferation. 

Risk of Data Misplacement: When Data Is Stored Outside Approved Environments

Misplaced data refers to information stored outside its approved environment. This can occur when data is in unauthorized or unverified cloud instances or shadow IT systems. Such misplacement heightens security risks and complicates compliance efforts.

A simple table can clarify the differences in risk levels and possible mitigation strategies for various data storage environments:

Risk of Insufficient Monitoring: Gaps in Real-Time Visibility of Rapid Data Movements

The high velocity of data flows in vast cloud environments makes tracking data challenging, and traditional monitoring methods may fall short. 

The rapid data movement means that data proliferation often outstrips traditional monitoring efforts. Meanwhile, the sheer volume, variety, and velocity of data require risk analysis tools that are built for scale. 

Legacy systems typically struggle with these issues, making it difficult for you to maintain up-to-date oversight and achieve a comprehensive security posture. Explore Sentra's blog on data movement risks for additional details.

Limitations of Legacy Data Security Solutions

When evaluating how to manage and monitor cloud data, it’s clear that traditional security tools fall short in today’s complex, cloud-native environments.

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Older security solutions (built for the on-prem era!) were designed for static environments, while today's dynamic cloud demands modern, more scalable approaches. Legacy data classification methods, as discussed in this Sentra analysis, also fail to manage unstructured data effectively.

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Let’s take a deeper look at their limitations:

• Inadequate data classification: Traditional data classification often relies on manual processes that fail to keep pace with real-time cloud operations. Manual classification is inefficient and prone to error, making it challenging to quickly identify and secure sensitive information.
  
  • Such outdated methods particularly struggle with unstructured data management, leaving gaps in visibility.
• ‍Scalability issues: As your enterprise grows and embraces the cloud, the volume of data you must handle also grows exponentially. When this happens, legacy systems cannot keep up. They lag behind and are slow to respond to potential risks, exposing your company to possible security breaches.
  
  • Modern requirements for cloud data management and monitoring call for solutions that scale with your business.

• High operational costs: Maintaining outdated security tools can be expensive. Legacy systems often incur high operational costs due to manual oversight, taxing cloud compute consumption, and inefficient processes. 
  
  • These costs can escalate quickly, especially compared to cloud-native solutions offering automation, efficiency, and streamlined management.

To address these risks, it's essential to have a strategy that shows you how to monitor data as it moves, ensuring that sensitive files never end up in unapproved environments.

Best Practices for Cloud Data Monitoring and Protection

In an era of rapidly evolving cloud environments, implementing a cohesive cloud data monitoring strategy that integrates actionable recommendations is essential. This approach combines automated data discovery, real-time monitoring, robust access governance, and continuous compliance validation to secure sensitive cloud data and address emerging threats effectively.

Automated Data Discovery and Classification

Implementing an agentless, cloud-native solution enables you to continuously discover and classify sensitive data without any performance drawbacks. Automation significantly reduces manual errors and delivers real-time insights for robust and efficient data monitoring.

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Benefits include:

• Continuous data discovery and classification
• Fewer manual interventions
• Real-time risk assessment
• Lower operational costs through automation
• Simplified deployment and ongoing maintenance
• Rapid response to emerging risks with minimal disruption

By adopting a cloud-native data security platform, you gain deeper visibility into your sensitive data without adding system overhead.

Real-Time Data Movement Monitoring

To prevent breaches, real-time cloud monitoring is critical. Receiving real-time alerts will empower you to take action quickly and mitigate threats in the event of unauthorized transfers or suspicious activities. 

A well-designed monitoring dashboard can visually display data flows, alert statuses, and remediation actions—all of which provide clear, actionable insights. Alerts can also flow directly to remediation platforms such as ITSM or SOAR systems.

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In addition to real-time dashboards, implement automated alerting workflows that integrate with your existing incident response tools. This ensures immediate visibility when anomalies occur for a swift and coordinated response. Continuous monitoring highlights any unusual data movement, helping security teams stay ahead of threats in an environment where data volumes and velocities are constantly expanding.

Robust Access Governance

Only authorized parties should be able to access and utilize sensitive data. Maintain strict oversight by enforcing least privilege access and performing regular reviews. This not only safeguards data but also helps you adhere to the compliance requirements of any relevant regulatory standards.

A checklist for robust governance might include:

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• Implementation of role-based and attribute-based access control
• Periodic access audits
• Integration with identity management systems

Ensuring Compliance and Data Privacy

Adhering to data privacy regulations that apply to your sector or location is a must. Continuous monitoring and proactive validation will help you identify and address compliance gaps before your organization is hit with a security breach or legal violation. Sentra offers actionable steps related to various regulations to solidify your compliance posture.

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Integrating automated compliance checks into your security processes helps you meet regulatory requirements. To learn more about scaling your security infrastructure, refer to Sentra’s guide to achieving exabyte-scale enterprise data security.

Beyond tools and processes, cultivating a security-minded culture is critical. Conduct regular training sessions and simulated breach exercises so that everyone understands how to handle sensitive data responsibly. Encouraging active participation and accountability across the organization solidifies your security posture, bridging the gap between technical controls and human vigilance.

Sentra Addresses Cloud Data Monitoring Challenges

Sentra's platform complements your current observability tools, enhancing them with robust data security capabilities. Let’s explore how Sentra addresses common challenges in cloud data monitoring.

Exabyte-Scale Mastery: Navigating Expansive Data Ecosystems

Sentra’s platform is designed to handle enormous data volumes with ease. Its distributed architecture and elastic scaling provide comprehensive oversight and ensure high performance as data proliferation intensifies. The platform's distributed architecture and elastic scaling capabilities guarantee high performance, regardless of data volume.

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Key features:

• Distributed architecture for high-volume data
• Elastic scaling for dynamic cloud environments
• Integration with primary cloud services

Seamless Automation: Transforming Manual Workflows into Continuous Security

By automating data discovery, classification, and monitoring, Sentra eliminates the need for extensive manual intervention. This streamlined approach provides uninterrupted protection and rapid threat response. 

Automation is essential for addressing the challenges of data sprawl without compromising system performance.

Deep Insights & Intelligent Validation: Harnessing Context for Proactive Risk Detection

Sentra distinguishes itself by providing deep contextual analysis of your data. Its intelligent validation process efficiently detects anomalies and prioritizes risks, enabling precise and proactive remediation. 

This capability directly addresses the primary concern of achieving continuous, real-time monitoring and ensuring precise, efficient data protection.

Unified Security: Integrating with your Existing Systems for Enhanced Protection

One of the most significant advantages of Sentra's platform is its seamless integration with your current SIEM and SOAR tools. This unified approach allows you to maintain excellent observability with your trusted systems while benefiting from enhanced security measures without any operational disruption.

Conclusion

Effective cloud data monitoring is achieved by blending the strengths of your trusted observability tools with advanced security measures. By automating data discovery and classification, establishing real-time monitoring, and enforcing robust access governance, you can safeguard your data against emerging threats. 

Elevate your operations with an extra layer of automated, cloud-native security that tackles data sprawl, proliferation, and compliance challenges. After carefully reviewing your current security and identifying any gaps, invest in modern tools that provide visibility, protection, and resilience.

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Maintaining cloud security is a continuous task that demands vigilance, innovation, and proactive decision-making. Integrating solutions like Sentra's platform into your security framework will offer robust, scalable protection that evolves with your business needs. The future of your data security is in your hands, so take decisive steps to build a safer, more secure cloud environment.

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At RSAC 2025, I had the opportunity to speak with Adrian Sanabria about one of the most pressing and complex challenges facing security teams today: data security. Since then, the urgency around the future of data security has only intensified.

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We're watching a major inflection point unfold across industries. Organizations are generating and storing more data than ever, while simultaneously adopting AI at a pace that outstrips most security programs. At the same time, regulators are enforcing data privacy with increasing sharpness. These trends all converge on one critical question:

Do you know where your sensitive data is - and who can access it?

If the answer is no, then it's time to rethink your approach.

Data is Now The Most Valuable, And Volatile Asset

For years, security tools have operated largely without visibility into the data itself. We've focused on endpoints, perimeters, and identities - all essential layers. But in 2025, that’s no longer sufficient.

Data is now the most valuable, and volatile asset most companies have. We’re seeing this in breach investigations, where the root cause often traces back to unmonitored or duplicated sensitive data left in the wrong place. We're seeing it in AI deployments, where teams rush to fine-tune models or deploy copilots without knowing what's inside the datasets they’re exposing. And we’re certainly seeing it in regulatory fines, many of which stem from nothing more than storing customer data longer than necessary, in the wrong place, or in unsecured formats.

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What all of this underscores is a simple truth: you can’t protect what you can’t see.

The Role of DSPM in the Future of Data Security

At Sentra, we’ve built our platform around a core philosophy that Data Security Posture Management (DSPM) is not just a security tool, it’s the future of data security, an enabler of responsible innovation. The foundation starts with sensitive data discovery. Most organizations are surprised by how much sensitive data exists outside expected systems- in backups, temporary stores, or SaaS apps that were never properly offboarded. From there, classification adds context. It’s not enough to label something as “PII”, we need to understand how sensitive it is, who owns it, how it is being used, and how it should be governed.

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We built Sentra as a cloud-native solution from day one. That means it works across IaaS, SaaS, PaaS, and even on-prem environments without needing agents or pulling data outside the customer’s environment. That last point is non-negotiable for us. As a security company, we believe strongly that extracting customer data for analysis creates unnecessary risk and liability.

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To support classification at scale, especially for unstructured data, we developed our own language models using open-source LLMs. This provides the deep contextual understanding needed to accurately label large volumes of data all while maintaining cost efficiency and avoiding unnecessary compute overhead.

AI, Risk, and Responsibility in Data Securityy

One of the biggest shifts we’re seeing in the market is how AI has elevated data security from a technical concern to a boardroom issue. Security teams are now being asked to approve large-scale data usage for AI training, RAG systems, copilots, and internal assistants. But very few have the tools to answer basic questions about what’s in those datasets.

I’ve worked with customers who only realized after deploying AI that they had been exposing medical records, credentials, or confidential meeting data to the model. Once it’s in, you can’t pull it back. That’s why data classification and risk detection must come before any AI integration.

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This is precisely the use case we had in mind when we built Sentra’s Data Security for AI Module. It helps teams scan, assess, and verify the contents of data before it ever touches a model. The goal isn’t to slow down innovation - it’s to make it safer, auditable, and repeatable.

Proactive Risk Management Helps Enterprises Ship Faster

One of the most exciting developments we’ve seen for the future of data security is how quickly Sentra’s data security platform becomes a strategic asset for enterprise data risk management. Time to value is fast in many cases, our customers discover major data risks just days after deployment. But beyond those early wins, the real power lies in alignment.

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When security leaders can map data to risk, compliance, and governance frameworks, and do so continuously, they’re no longer operating reactively. They’re enabling the business, helping teams ship faster with fewer unknowns, and building trust around how AI and data are managed.

At scale, this kind of maturity is the difference between organizations that can confidently embrace generative AI and those that will always be playing catch-up.

A Final Word

From my time in the Israeli Defense Forces and Unit 8200 to helping enterprises build modern security programs, I’ve seen one truth over and over again: data left behind is data exposed. The volume may grow, the threats may change, but this principle doesn’t.

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In 2025, securing data is no longer an aspiration, it’s a baseline. Whether you’re preparing for your next AI initiative, facing regulatory audits, or just trying to get visibility into sprawling cloud environments, DSPM should be your first step. At Sentra, we’re proud to help lead this change. And we believe the organizations that take control of their data today will be the ones best positioned to lead tomorrow.

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“If you don’t know where your sensitive data is, how can you protect it?”

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It’s a simple question, but for many security and compliance teams, it’s nearly impossible to answer. When a Fortune 500 company recently paid millions in fines due to improperly stored customer data on an unmanaged cloud bucket, the real failure wasn’t just a misconfiguration. It was a lack of visibility.

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Some in the industry are starting to refer to this challenge as "data blindness".

What Is Data Blindness?

Data Blindness refers to an organization’s inability to fully see, classify, and understand the sensitive data spread across its cloud, SaaS, and hybrid environments.

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It’s not just another security buzzword. It’s the modern evolution of a very real problem: traditional data protection methods weren’t built for the dynamic, decentralized, and multi-cloud world we now operate in. Legacy DLP tools or one-time audits simply can’t keep up.

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Unlike general data security issues, Data Blindness speaks to a specific kind of operational gap: you can’t protect what you can’t see, and most teams today are flying partially blind.

Why Data Blindness Is Getting Worse

What used to be a manageable gap in visibility has now escalated into a full-scale operational risk. As organizations accelerate cloud adoption and embrace SaaS-first architectures, the complexity of managing sensitive data has exploded. Information no longer lives in a few centralized systems, it’s scattered across AWS, Azure, and GCP instances, and a growing stack of SaaS tools, each with its own storage model, access controls, and risk profile.

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At the same time, shadow data is proliferating. Sensitive information ends up in collaboration platforms, forgotten test environments, and unsanctioned apps - places that rarely make it into formal security inventories. And with the rise of generative AI tools, a new wave of unstructured content is being created and shared at scale, often without proper visibility or retention controls in place.

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To make matters worse, many organizations are still operating with outdated identity and access frameworks. Stale permissions and misconfigured policies allow unnecessary access to critical data, dramatically increasing the potential impact of both internal mistakes and external breaches.

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In short, the cloud hasn’t just moved the data, it’s multiplied it, fragmented it, and made it harder than ever to track. Without continuous, intelligent visibility, data blindness becomes the default.

The Hidden Risks of Operating Blind

When teams don’t have visibility into where sensitive data lives or how it moves, the consequences stack up quickly:

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• Compliance gaps: Regulations like GDPR, HIPAA, and PCI-DSS demand accurate data inventories, privacy adherence, and prompt response to DSARs. Without visibility, you risk fines and legal exposure.
  
  
• Breach potential: Blind spots become attack vectors. Misplaced data, overexposed buckets, or forgotten environments are easy targets.
  
  
• Wasted resources: Scanning everything (just in case) is expensive. Without prioritization, teams waste cycles on low-risk data.
  
  
• Trust erosion: Customers expect you to know where their data is and how it’s protected. Data blindness isn’t a good look.
  
  

Do You Have Data Blindness? Here Are the Signs

• Your security team can’t confidently answer, “Where is our most sensitive data and who has access to it?”
  
  
• Data inventories are outdated, or built on manual tagging and spreadsheets.
  
  
• You’re still relying on legacy DLP tools with poor context and high false positives.
  
  
• Incident response is slow because it’s unclear what data was touched or how sensitive it was.

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Sound familiar? You’re not alone.

Breaking Free from Data Blindness

Solving data blindness starts with visibility, but real progress comes from turning that visibility into action. Modern organizations need more than one-off audits or static reports. They need continuous data discovery that scans cloud, SaaS, and on-prem environments in real time, keeping up with the constant movement of data.

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But discovery alone isn’t enough. Classification must go beyond content analysis, it needs to be context-aware, taking into account where the data lives, who has access to it, how it’s used, and why it matters to the business. Visibility must extend to both structured and unstructured data, since sensitive information often hides in documents, PDFs, chat logs, and spreadsheets. And finally, insights need to be integrated into existing security and compliance workflows. Detection without action is just noise.

How Sentra Solves Data Blindness

At Sentra, we give security and privacy teams the visibility and context they need to take control of their data - without disrupting operations or moving it out of place. Our cloud-native DSPM (Data Security Posture Management) platform scans and classifies data in-place across cloud, SaaS, and on-prem environments, with no agents or data removal required.

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Sentra uses AI-powered, context-rich classification to achieve over 95% accuracy, helping teams identify truly sensitive data and prioritize what matters most. We provide full coverage of structured and unstructured sources, along with real-time insights into risk exposure, access patterns, and regulatory posture, all with a cost-efficient scanning model that avoids unnecessary compute usage.

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One customer reduced their shadow data footprint by 30% in just a few weeks, eliminating blind spots that their legacy tools had missed for years. That’s the power of visibility, backed by context, at scale.

The Bottom Line: Awareness Is Step One

Data Blindness is real, but it’s also solvable. The first step is acknowledging the problem. The next is choosing a solution that brings your data out of the dark, without slowing down your teams or compromising security.

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If you’re ready to assess your current exposure or just want to see what’s possible with modern data security, you can take a free data blindness assessment, or talk to our experts to get started.

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When we started Sentra three years ago, we had a hypothesis: organizations were drowning in data they couldn't see, classify, or protect. What we didn't anticipate was how brutally honest our customers would be about what actually works, and what doesn't.

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This week, Gartner named Sentra a "Customer's Choice" in their Peer Insights Voice of the Customer report for Data Security Posture Management. The recognition is based on over 650 verified customer reviews, giving us a 4.9/5 rating with 98% willing to recommend us.

The Accuracy Obsession Was Right

The most consistent theme across hundreds of reviews? Accuracy matters more than anything else.

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"97.4% of Sentra's alerts in our testing were accurate! By far the highest percentage of any of the DSPM platforms that we tested."

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‍"Sentra accurately identified 99% of PII and PCI in our cloud environments with minimal false positives during the POC."

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But customers don't just want data discovery—they want trustworthy data discovery. When your DSPM tool incorrectly flags non-sensitive data as critical, teams waste time investigating false leads. When it misses actual sensitive data, you face compliance gaps and real risk. The reviews validate what we suspected: if security teams can't trust your classifications, the tool becomes shelf-ware. Precision isn't a nice-to-have—it's everything.

How Sentra Delivers Time-to-Value

Another revelation: customers don't just want fast deployment, they want fast insights.

"Within less than a week we were getting results, seeing where our sensitive data had been moved to."

"We were able to start seeing actionable insights within hours."

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I used to think "time-to-value" was a marketing term. But when you're a CISO trying to demonstrate ROI to your board, or a compliance officer facing an audit deadline, every day matters. Speed isn’t a luxury in security, it’s a necessity. Data breaches don't wait for your security tools to finish their months-long deployment cycles. Compliance deadlines don't care about your proof-of-concept timeline. Security teams need to move at the speed of business risk.

The Honesty That Stings (And Helps)

But here's what really struck me: our customers were refreshingly honest about our shortcomings.

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"The chatbot is more annoying than helpful."

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‍"Currently there is no SaaS support for something like Salesforce."

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"It's a startup so it has all the advantages and disadvantages that those come with."

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As a founder, reading these critiques was... uncomfortable. But it's also incredibly valuable. Our customers aren't just users, they're partners in our product evolution. They're telling us exactly where to invest our engineering resources.

The Salesforce integration requests, for instance, showed up in nearly every "dislike" section. Message received. We're shipping SaaS connectors specifically because it’s a top priority for our customers.

What Gartner Customer Choice Trends Reveal About the DSPM Market

Analyzing 650 reviews across 9 vendors revealed something fascinating about our market's maturity. Customers aren't just comparing features, they're comparing outcomes.

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The traditional data security playbook focused on coverage: "How many data sources can you scan?" But customers are asking different questions:

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• How accurate are your findings?
• How quickly can I act on your insights?
• How much manual work does this actually eliminate?

This shift from inputs to outcomes suggests the DSPM market is maturing rapidly. 

The Gartner Voice of the Customer Validated

Perhaps the most meaningful insight came from what customers didn't say. I expected more complaints about deployment complexity, integration challenges, or learning curves. Instead, review after review mentioned how quickly teams became productive with Sentra.

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"It was also the fastest set up."

"Quick setup and responsive support."

"The platform is intuitive and offers immediate insights."

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This tells me we're solving a real problem in a way that feels natural to security teams. The best products don't just work, they feel inevitable once you use them.

The Road Ahead: Learning from Gartner Choice Recognition

These reviews crystallized our 2025 roadmap priorities:

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1. SaaS-First Expansion: Every customer asked for broader SaaS coverage. We're expanding beyond IaaS to support the applications where your most sensitive data actually lives. Our mission is to secure data everywhere.

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2. AI Enhancement: Our classification engine is industry-leading, but customers want more. We're building contextual AI that doesn't just find data, it understands data relationships and business impact.

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