Agentic AI
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Artificial Intelligence & Machine Learning
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Data Loss Prevention (DLP)
New Investment Will Extend DSPM Technology Into Broad Data Protection Capabilities
Michael Novinson (MichaelNovinson) •
November 5, 2026
Yoav Regev, co-founder and CEO, Sentra (Image: Sentra)
A startup founded by an Israeli Military Intelligence colonel raised $150 million to understand and control the data that AI models and agents can access.See Also: Safeguarding Your Business in the Era of Autonomous AI Agents
The Capital One Ventures-led Series C funding will help New York-based Sentra build data loss protection into a broader data security platform, develop AI governance and agentic AI capabilities, strengthen identity and access controls, and continue to invest in core data discovery and classification, said co-founder and CEO Yoav Regev. He said the money will support product development as organizations adopt AI.
“What we could see in the last two years is that AI is a data problem, and there is no way to solve those problems that are coming with AI without the data,” Regev told ISMG. “The agentic era has just started, and we are going to see it exploding in the next few years.”
Sentra, founded in 2021, employs 210 people and has raised $253 million in four rounds of outside funding, with Key1 Capital leading the firm’s $50 million Series B funding round in April 2025. The company has been led since its its inception by Regev, who spent nearly 24 years in Israel’s Unit 8200, culminating in a stint as the head of its cyber department (see: Startup Sentra Nabs $50M to Boost AI-Powered Data Protection).
Helping Organizations Prepare for AI Adoption
Customers increasingly want an end-to-end data security platform rather than isolated capabilities, and Regev sees DLP, DSPM, AI security and identity as connected parts of that platform. DLP decisions are more effective when informed by data context such as what the data is, where it resides and who can access it, rather than being made without that context at endpoints or in email, Regev said.
“Organizations need a full data security platform end to end,” Regev said. “You cannot find such a solution these days. DLP is one aspect, the infrastructure that we have today is one aspect, and AI security and the combination between identities and data is one of the most important parts today to solve the agentic problem.”
Regev believes Sentra’s investments in identity will provide an access intelligence layer that maps people and non-human identities – including agents – and connects those identities to data. This could support adaptive, time-limited permissions, so agents receive only the access needed for a particular task, Regev said.
“We are going to connect the dots between the identities, mainly the non-human identities, such as the agents and the data,” Regev said. “With that, you can ensure adaptive permissions because you cannot follow the agents, so you need those identities to get on-demand permissions and on-demand access to the data.”
Sentra currently helps organizations prepare for AI adoption by identifying sensitive data and reducing the risk that it is exposed to the wrong models or surfaced to unauthorized users through tools such as copilots, Regev said. Current use cases for Sentra include data hygiene, AI readiness and addressing excessive permissions in AI workflows, Regev said.
“You don’t want to take your sensitive data and push it to the models,” Regev said. “On the other hand, you don’t want your Copilot or Gemini, or whichever solution you have, to retrieve the wrong data to the wrong person.”
How DSPM, Data Discovery Support Newer Use Cases
Sentra is expanding toward AI governance and agentic AI security, with a focus on understanding which models and agents exist and what data they can reach, Regev said. This opportunity broadens Sentra’s potential buyer group beyond CISOs to include CIOs and CDOs, Regev said. He characterized data, security and technology leaders as increasingly sharing responsibility for AI adoption and governance (see: Sentra CEO: Small AI Models Cut Cost of Data Classification).
“Today, you can see CIOs, CDOs and CISOs share the same problems,” Regev said. “They share the understanding that the data layer is so important in many different aspects.”
Continued investment in core DSPM and discovery capabilities is essential to support newer use cases, Regev said. Priorities for Sentra include improving classification, expanding data coverage, increasing performance and strengthening the ability to identify data, add context, and associate it with access and identity information, according to Regev.
“You cannot solve it without the right engine, without the right basics, the foundation of finding the data, classifying the data and adding the context of who has access to the data and the identities that are correlated to that,” Regev said. “You’re not going to buy a new car without the best engine, and we want to always have the best engine.”
Sentra’s differentiation is its ability to connect AI security and DLP capabilities to a deep understanding of organizational data, including data discovery, classification and context, Regev said. Sentra can win head to head against larger rivals such as Proofpoint and Cyera through its technology, keeping customer data in the customer environment, discovery capabilities, operational cost and classification accuracy, he said.
“Many of the other startups deal with a very specific part or feature. Many of them do a great job,” Regev said. “But it’s very hard for them to connect it to the real asset, the data, and that’s what we do very differently from anyone else.”
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