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How Does DSPM Safeguard Your Data When You Have CSPM/CNAPP

September 10, 2024
4
Min Read
Data Security

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.

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.

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.

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.

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. 

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. 

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: 

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. 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.

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

  • 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.

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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Yair brings a wealth of experience in cybersecurity and data product management. In his previous role, Yair led product management at Microsoft and Datadog. With a background as a member of the IDF's Unit 8200 for five years, he possesses over 18 years of expertise in enterprise software, security, data, and cloud computing. Yair has held senior product management positions at Datadog, Digital Asset, and Microsoft Azure Protection.

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David Stuart
David Stuart
January 28, 2026
3
Min Read

Data Privacy Day: Why Discovery Isn’t Enough

Data Privacy Day: Why Discovery Isn’t Enough

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.

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.

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.

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?

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.

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.

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.

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.

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?”

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.

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.

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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David Stuart
David Stuart
January 27, 2026
4
Min Read

DSPM for Modern Fintech: From Masking to AI-Aware Data Protection

DSPM for Modern Fintech: From Masking to AI-Aware Data Protection

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.

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.

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.

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.

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.

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.

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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Romi Minin
Romi Minin
Nikki Ralston
Nikki Ralston
January 26, 2026
4
Min Read

How to Choose a Data Access Governance Tool

How to Choose a Data Access Governance Tool

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.

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:

  • 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:

  • 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.

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

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

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:

  • 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.

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.

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.

How do organizations reduce over-permissioned access safely?

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

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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