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It's Time to Embrace Cloud DLP and DSPM

March 11, 2024

Min Read

Data Loss Prevention

David Stuart

Senior Director, Product Marketing

What’s the best way to prevent data exfiltration or exposure? In years past, the clear answer was often data loss prevention (DLP) tools. But today, the answer isn’t so clear — especially in light of the data democratization trend and for those who have adopted multi-cloud or cloud-first strategies.

Data loss prevention (DLP) emerged in the early 2000s as a way to secure web traffic, which wasn’t encrypted at the time. Without encryption, anyone could tap into data in transit, creating risk for any data that left the safety of on-premise storage. As Cyber Security Review describes, “The main approach for DLP here was to ensure that any sensitive data or intellectual property never saw the outside web. The main techniques included (1) blocking any actions that copy or move data to unauthorized devices and (2) monitoring network traffic with basic keyword matching.”

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Although DLP has evolved for securing endpoints, email and more, its core functionality has remained the same: gatekeeping data within a set perimeter. But, this approach simply doesn’t perform well in cloud environments, as the cloud doesn’t have a clear perimeter. Instead, today’s multi-cloud environment includes constantly changing data stores, infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS) and more.

And thanks to data democratization, people across an organization can access all of these areas and move, change, or copy data within seconds. Cloud applications do so as well—even faster.

Traditional DLP tools weren’t built for cloud-native environments and can cause significant challenges for today’s organizations. Data security teams need a new approach, purpose-built for the realities of the cloud, digital transformation and today’s accelerated pace of innovation.

Why Traditional DLP Isn’t Ideal for the Cloud

Traditional DLPs are often unwieldy for the engineers who must work with the solution and ineffective for the leaders who want to see positive results and business continuity from the tool. There are a few reasons why this is the case:

1. Traditional DLP tools often trigger false alarms.

Traditional DLPs are prone to false positives. Because they are meant to detect any sensitive data that leaves a set perimeter, these solutions tend to flag normal cloud activities as security risks. For instance, traditional DLP is notorious for erroneously blocking apps and services in IaaS/PaaS environments. These “false positives” disrupt business continuity and innovation, which is frustrating for users who want to use valuable cloud data in their daily work. Not only do traditional DLPs block the wrong signals, but they also overlook the right ones, such as suspicious activities happening over cloud-based applications like Slack, Google Drive or generative AI/LLM apps. Plus, traditional DLP doesn’t follow data as users move, change or copy it, meaning it can easily miss shadow data.

2. Traditional DLP tools cause alert fatigue.

In addition, these tools lack detailed data context, meaning that they can’t triage alerts based on severity. Combine this factor with the high number of false positives, and teams end up with an overwhelming list of alerts that they must sort manually. This reality leads to alert fatigue and can cause teams to overlook legitimate security issues.

3. Traditional DLP tools rely on lots of manual intervention.

Traditional DLP deployment and maintenance take up lots of time and resources for a cloud-based or hybrid organization. For instance, teams must often install several legacy agents and proxies across the environment to make the solution work accurately. Plus, these legacy tools rely on clear-cut data patterns and keywords to uncover risk. These patterns are often hidden or nonexistent because they are often disguised or transformed in the data that exists in or moves to cloud environments. This means that teams must manually tune their DLP solution to align with what their sensitive cloud data actually looks like. In many cases, this manual intervention is very difficult—if not impossible—since many cloud pipelines rely on ETL data, which isn’t easy to manually alter or inspect.

Additionally, today’s organizations use vast amounts of unstructured data within cloud file shares such as Sharepoint. They must parse through tens or even hundreds of petabytes of this unstructured data, making it challenging to find hidden sensitive data. Traditional DLP solutions lack the technology that would make this process far easier, such as AI/ML analysis.

Cloud DLP: A Cloud-Native Approach to Data Loss Prevention

Because the cloud is so different from traditional, on-premise environments, today’s cloud-based and hybrid organizations need a new solution. This is where a cloud DLP solution comes into the picture. We are seeing lots of cloud DLP tools hit the market, including solutions that fall into two main categories:

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SaaS DLP products that leverage APIs to provide access control. While these products help to protect from loss within some SaaS applications, they are limited in scope, only covering a small percentage of the cloud services that a typical cloud-native organization uses. These limitations mean that a SaaS DLP product can’t provide a truly comprehensive view of all cloud data or trace data lineage if it’s not based in the cloud.

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IaaS + PaaS DLP products that focus on scanning and classifying data. Some of these tools are simply reporting tools that uncover data but don’t take action to remediate any issues. This still leaves extra manual work for security teams. Other IaaS + PaaS DLP offerings include automated remediation capabilities but can cause business interruptions if the automation occurs in the wrong situation.

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To directly address the limitations inherent in traditional DLPs and avoid these pitfalls, next-generation cloud DLPs should include the following:

Scalability in complex, multi-cloud environments
Automated prioritization for detected risks based on rich data context
Auto-detection and remediation capabilities that use deep context to correct configuration issues, creating efficiency without blocking everyday activities
Integration and workflows that are compatible with your existing environments
Straightforward, cloud-native agentless deployment without extensive tuning or maintenance

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Attribute	Cloud DLP	DSPM	DDR
Security Use Case	Data Leakage Prevention	Data Posture Improvement, Compliance	Threat Detection and Response
Environments	SaaS, Cloud Storage, Apps	Public Cloud, SaaS and OnPremises	Public Cloud, SaaS, Networks
Risk Prioritization	Limited: based only on predefined policies - not based on discovered data or data context	Analyzes Data Context, Access Controls, and Vulnerabilities	Threat Activity Context such as anomalous traffic, volume, access
Remediation	Block or Redact Data Transfers, Encryption, Alert	Alerts, IR/Tool Integration & Workflow Initiation	Alerts, Revoke Users/Access, Isolate Data Breach

Further Enhancing Cloud DLP by Integrating DSPM & DDR

While Cloud Data Loss Prevention (DLP) helps to secure data in multi-cloud environments by preventing loss, DSPM and DDR capabilities can complete the picture. These technologies add contextual details, such as user behavior, risk scoring and real-time activity monitoring, to enhance the accuracy and actionability of data threat and loss mitigation. Data Security Posture Management (DSPM) enforces good data hygiene no matter where the data resides. It takes a proactive approach, significantly reducing data exposure by preventing employees from taking risky actions in the first place. Data Detection and Response (DDR) alerts teams to the early warning signs of a breach, including suspicious activities such as data access by an unknown IP address. By bringing together Cloud DLP, DSPM and DDR, your organization can establish holistic data protection with both proactive and reactive controls. There is already much overlap in these technologies. As the market evolves, it is likely they will continue to combine into holistic cloud-native data security platforms.

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Sentra’s data security platform brings a cloud-native approach to DLP by automatically detecting and remediating data risks at scale. Built for complex multi-cloud and premise environments, Sentra empowers you with a unified platform to prioritize all of your most critical data risks in near real-time.

‍Request a demo to learn more about our cloud DLP, DSPM and DDR offerings.

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David Stuart

Senior Director, Product Marketing

David Stuart is Senior Director of Product Marketing for Sentra, a leading cloud-native data security platform provider, where he is responsible for product and launch planning, content creation, and analyst relations. Dave is a 20+ year security industry veteran having held product and marketing management positions at industry luminary companies such as Symantec, Sourcefire, Cisco, Tenable, and ZeroFox. Dave holds a BSEE/CS from University of Illinois, and an MBA from Northwestern Kellogg Graduate School of Management.

Latest Blog Posts

Asaf Kochan

July 9, 2025

Min Read

Data Security

Data Security in 2025: Why DSPM Is Now a Business Imperative

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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Team Sentra

July 2, 2025

Min Read

Data Security

Data Blindness: The Hidden Threat Lurking in Your Cloud

“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.

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.

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.

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.

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.

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.

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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Yoav Regev

June 12, 2025

Min Read

Data Security

Why Sentra Was Named Gartner Peer Insights Customer Choice 2025

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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‍3. Remediation Automation: Customers love our visibility but want more automated remediation. We're moving beyond recommendations to actual risk mitigation.

A Personal Thank You

To the customers who contributed to our Sentra Gartner Peer Insights success: thank you. Building a startup is often a lonely journey of best guesses and gut instincts. Your feedback is the compass that keeps us pointed toward solving real problems.

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To the security professionals reading this: your honest feedback (both praise and criticism) makes our products better. If you're using Sentra, please keep telling us what's working and what isn't. If you're not, I'd love to show you what earned us Customer Choice 2025 recognition and why 98% of our customers recommend us.

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The data security landscape is evolving rapidly. But with customers as partners and recognition like Gartner Peer Insights Customer Choice 2025, I'm confident we're building tools that don't just keep up with threats, they help organizations stay ahead of them.

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