Ron Reiter, CTO and Co-Founder of Sentra – Interview Series

Ron Reiter, CTO and Co-Founder of Sentra, is a seasoned entrepreneur and cybersecurity expert with deep cloud expertise, who has built innovative technology solutions throughout his more than two decades in software development; he co-founded and leads technology at Sentra, a data cybersecurity firm focused on helping enterprises secure their cloud data, and earlier co-founded Crosswise (acquired by Oracle for $50 M), while also serving over six years as an engineering director at Oracle/Crosswise overseeing Oracle Data Cloud products and teams.

Sentra is a cloud-native data security platform that uses AI-driven discovery, classification and contextual analysis to give organizations full visibility and control over sensitive data across cloud, hybrid and on-premises environments, helping them assess risk, enforce governance, meet compliance and prevent data exposure at scale across modern multi-cloud and AI workflows.

You’ve founded multiple companies across cybersecurity and data infrastructure. What inspired you to create Sentra, and how did your experience at Crosswise and Oracle influence the company’s early direction?

What pushed me to start Sentra was a pattern I kept seeing repeat itself. At Crosswise and later at Oracle, data was always the center of gravity. It was where value lived, but also where risk accumulated. Yet most security tools treated data as something static, something you discovered once and then assumed was under control.

As cloud adoption accelerated and organizations started experimenting with AI, that assumption stopped holding. Data was moving constantly, being copied, transformed and accessed by systems no one fully tracked. I wanted to build a company that started with data as a living asset, something you continuously understand and govern, rather than something you inventory once and forget. That idea shaped Sentra from day one.

Sentra focuses on giving organizations full control and visibility over their cloud data. What core problem were you most determined to solve when you began architecting the platform?

The core problem was false confidence. Many organizations believed they understood their data posture, but that confidence was based on partial visibility. They knew where some sensitive data lived, but not all of it, and they rarely had a clear picture of how that data was being accessed or reused over time.

We set out to close that gap. Not just by discovering data, but by maintaining an ongoing understanding of what data exists, how sensitive it is and who or what can access it. Without that foundation, everything else in security becomes reactive.

You’ve spoken about the importance of accuracy in modern data security. What makes achieving high accuracy at massive cloud scale so challenging, and how did your team approach that problem differently?

Accuracy becomes difficult at scale because context matters. As environments grow, data becomes more unstructured and more specific to how a business actually operates. Simple pattern matching and general-purpose models work reasonably well in smaller environments, but they tend to break down as data volumes grow and use cases become more complex.

We saw this firsthand in enterprise evaluations where accuracy degraded as customers moved from tens of terabytes to petabytes of unstructured data. Our approach was to design classification around context, and to be disciplined about efficiency. Accuracy that only works at small scale or requires excessive compute is not useful in real enterprise environments.

Scanning and securing data across distributed cloud environments is notoriously difficult. What architectural decisions allow Sentra to operate efficiently across multiple clouds and data stores?

We assumed from the start that customers would operate across multiple clouds, SaaS platforms, and hybrid environments. That pushed us to avoid designs that depend on heavy data movement or constant full rescans, which don’t perform well as environments grow.

Instead, we focused on maintaining visibility as environments change and minimizing unnecessary overhead. That design choice shows up in reliability and cost predictability, especially in large, complex environments.

As AI agents, copilots, and automated workflows become embedded into enterprise systems, what new categories of data-security risk do you believe enterprises are still underestimating?

The biggest blind spot is non-human access. AI agents, integrations and automated workflows now access sensitive data continuously, often outside the controls designed for human users.

These systems don’t log in the same way people do, and they don’t trigger traditional alerts. Treating them as just another user is a mistake. Enterprises need to understand what these systems can access and ensure those permissions stay aligned with intent, otherwise risk scales faster than teams can respond.

Sentra uses a model-driven approach to classify and secure sensitive data. How do you balance model performance, operational cost, and scalability when building for enterprise workloads?

Balance comes from being deliberate about how models are used. Not every problem requires the largest or most general model. We focus on using small language models (SLMs) that are well suited to classification tasks and can operate efficiently in large environments.

This allows us to maintain strong accuracy while keeping operating costs low and predictable. For enterprise security teams, consistency and reliability matter as much as raw performance.

What is the biggest misconception you see among CISOs about securing cloud data in the AI era, and how should their strategies evolve?

A common misconception is that discovering data once is enough. In reality, cloud and AI environments change constantly. Data moves, permissions drift and new systems come online every week.

Strategies need to shift from periodic assessment to continuous governance. That means treating data security as an ongoing discipline rather than a project. The goal is not just to find risk, but to keep risk from reappearing as the environment evolves.

Data Security Posture Management (DSPM) has become a central layer of the modern cloud-security stack. In your view, what characteristics define a truly mature DSPM platform?

A mature DSPM platform does three things well. It has to understand data accurately, it has to operate reliably at large scale, and it has to support action rather than just reporting.

What we are seeing now is that many platforms look strong in POVs or early deployments, but struggle as environments grow and access patterns become more dynamic. Scans slow down, costs rise and accuracy erodes, especially with unstructured data. A mature DSPM platform is one that security teams still trust when data volumes reach production scale and AI systems are accessing data continuously. Trust at scale is what separates usable platforms from theoretical ones.

You’ve also invested in several cybersecurity startups. From that perspective, what do you think separates founders who succeed in this industry from those who struggle?

The founders who succeed tend to be very close to real customer pain. They resist the temptation to chase buzzwords or overbuild for edge cases, and instead focus on solving problems that show up repeatedly in production environments.

They also think about sustainability early. In security, winning a proof of concept is easy. Running reliably at scale for years is much harder. Founders who design for that reality from the start tend to last.

In 2026 and beyond, how do you expect data-security requirements to shift as organizations adopt decentralized architectures, autonomous AI systems, and increasingly complex data flows?

Data security will move from protecting locations to governing movement. As architectures decentralize and AI systems act autonomously, the question will no longer be where data sits, but how it flows and who or what can use it.

Organizations will need continuous visibility and policy enforcement that travels with the data itself. Those that can’t achieve that will find their AI initiatives slowed by risk and compliance concerns. Those that can will move faster, with confidence.

Thank you for the great interview, readers who wish to learn more should visit Sentra.