Florida Information Protection Act (FIPA): 30‑Day Data Breach Deadline and Compliance Checklist

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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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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