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Thoughts on Sentra and the Data Security Landscape After Our Series A

February 1, 2023
3
Min Read

By Asaf Kochan, Co-Founder and President, Sentra

Series A announcements are an exciting time for any startup, and we look forward to working with our new investors from Standard Investments, Munich re Ventures (MRV), Moore Strategic Ventures, and INT3 to grow the cloud data security and data security posture management (DSPM) categories. 

I wanted to take a moment to share some of my thoughts around what this round means for Sentra, cloud data security, and the growth of the DSPM category as a whole. 

Seeing is Believing: From Potential Customer to Investor

The most amazing part of this round is that we didn’t originally intend to raise money. We approached Standard Industries as a potential customer not an investor. It was incredible to see how bought-in the team was to Sentra’s approach to data security. They understood instantly the potential for securing not only Standard’s data, but the data of every cloud-first enterprise. The total addressable market for data security solutions is already large, and it’s growing every year, as more and more new companies are now cloud-native. The global need for solutions like Sentra was obvious to their team after seeing the product, and I’m excited to have a forward-thinking investor like Standard as part of our journey. 

It’s a Vote of Confidence in the Sentra Team and Product

Any Series A is first and foremost a vote of confidence. It’s an endorsement of the vision of the company, the approach the product is taking, and the potential of the core team to continue to grow the business. Anyone who has spoken with our talented team understands the level of expertise and perseverance they bring to every task, meeting, and challenge. I’m proud of the team we’ve built, and I’m excited to welcome many new Sentrans to the team in the coming months. 

As I mentioned, the round is also a mark of confidence of the development and direction of the product itself. Working with our existing customers, we’ve regularly added new features, integrations, and capabilities to our solution. As we continue to discover, classify, and secure larger amounts of data in all cloud environments, the benefits of a data centric approach become clear. We’ve successfully reduced the risks of catastrophic data breaches by reducing the data attack surface, improved relationships between engineering and security teams by breaking down silos, and even helped our customers reduce cloud costs by finding and eliminating duplicate data. 

Data Security is a Must, Not a Nice to Have

Raising money in the current economic climate is not to be taken for granted. The significant investment in Sentra’s vision speaks not only to the value provided by Sentra’s product and team, but also to how critical data security has become. Compliance, privacy, and security concerns are present regardless of how the NASDAQ is performing.

Certainly we’re seeing no slowdown in data security regulations. Global enterprises are now responsible for ensuring compliance with a growing number of data regulations from different government and commercial sources across the globe.  When it comes to security and IP protection, the threat of a catastrophic data breach is top of mind for all cloud security teams. As the reality sets in that breaches are not a matter of “if” but “when”, the logic of the data centric approach becomes clear: If you can’t prevent the initial breach, you can make sure your most sensitive data always has the proper security posture. In the future we’re building, not every breach will be newsworthy, because not every breach will involve sensitive data. This funding round demonstrates the growing acceptance that this is the direction cloud security is and should be heading. 

DSPM will Come to Dominate Cloud Security

There’s always some skepticism in the cyber world when a new category is created. 

  • Is the problem it claims to solve really that serious?
  • Can we just use existing paradigms and tools to address it?
  • Is implementing a new tool going to make a real difference for the business? 

These questions are valid, and any cyber company operating in a new space must address them forthrightly and clearly. We have been clear from the beginning - a data centric approach to security with DSPM is not a small step, but a giant leap forward. Data is the core asset of most companies, and that asset is now stored in the cloud. Old approaches will not be sufficient. This new round is led by investors who recognize this new reality and share our vision that we need to put data at the core of cloud security strategies.

I want to end by again emphasizing how thankful I am for having amazing investors, partners, and team members join us over the last 18 months. So much has been accomplished already, but the industry shift to data centric security has only just begun. I’m looking forward to continuing to protect the most important business asset in the world - our data.

For four years, he was the commander of Israel’s Unit 8200, leading the world's most innovative cyber team. During those years, he saw threat actors continuously exploit sensitive data that was improperly secured, and worked to mitigate the damage this was causing to both the public and private sectors. Reflecting on these experiences, it was clear to him that sensitive data had become the most important asset in the world. In the private sector, enterprises that were leveraging data to generate new insights, develop new products, and provide better experiences, were separating themselves from the competition. As data becomes more valuable it becomes more of a target. And as the amount of sensitive data grows, so does the importance of finding the most effective way to secure it. That’s why he co-founded Sentra, together with accomplished co-founders Yoav Regev, Ron Reiter, and Yair Cohen.

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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
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Nikki Ralston
Nikki Ralston
January 26, 2026
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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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