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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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DSPM Dirty Little Secrets: What Vendors Don’t Want You to Test

DSPM Dirty Little Secrets: What Vendors Don’t Want You to Test

Discover  What DSPM Vendors Try to Hide 

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

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

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

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

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

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

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

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

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

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

DSPM POV Red Flags 

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

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

Four DSPM POV Requirements That Expose the Truth

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

1. Scalability

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

2. Cost Efficiency

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

3. Accuracy

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

4. Unstructured Data Depth

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

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

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

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David Stuart
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January 28, 2026
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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
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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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