Sentra raises $150 million to secure enterprise data from AI agents

Sentra has raised $150 million led by Capital One Ventures to protect enterprise data from AI agents. Its platform identifies sensitive data and restricts...

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Sentra has raised $150 million in a funding round led by Capital One Ventures, bringing its total funding to over $250 million. The New York and Tel Aviv-based company offers a platform designed to protect sensitive enterprise data from AI agents by identifying and limiting their access within company systems.

With AI agents increasingly accessing large volumes of company files and customer data, many enterprises lack clarity on what data these agents can reach. Sentra addresses this by classifying sensitive data, enforcing least privilege access, and monitoring AI agent activity to prevent excessive data exposure.

The company has experienced rapid growth, with revenue in the tens of millions and a customer base of about 100 large enterprises across the US and Europe. Sentra's team includes former Unit 8200 commanders, and the platform operates entirely within the client's environment to avoid sending sensitive data offsite.

The $150 million round was led by Capital One Ventures, which also gained a board seat. Existing investors including Bessemer Venture Partners, Zeev Ventures, Standard Investments, Munich Re Ventures, and Key1 Capital participated. Sentra plans to use the funds to improve visibility into AI agents and copilots, enhance continuous least-privilege controls, and speed up containment of misbehaving agents.

Sentra intends to double its workforce, open a London office, and pursue US federal government contracts starting in late 2027. The company reports 300% year-over-year growth and a valuation estimated close to $1 billion.

Sentra also aims to deepen its technology for mapping data access and enforcing security policies in real time.

Competition in AI data security is intensifying, with other players raising large funding rounds and acquisitions in the space.

Sources

Source: Ventureburn

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