Sentra Raises $150M Series C to Expand Agentic – Unite.AI Data Security Platform

Sentra announced on November 4, 2026 that Capital One Ventures led a $150 million strategic investment in the agent-based data security company. The Series C funding will expand Sentra’s platform for managing enterprise data accessed by employees, applications and AI agents, as well as its go-to-market operations.

Capital One Ventures joins existing investors Bessemer Venture Partners, Zeev Ventures, Standard Investments, Monaco Re Ventures and Key1 Capital. A representative from Capital One will also join Sentra’s board of directors. Sentra posted year-over-year growth of more than 300% in the year before the announcement.

AI agents change the problem of data security

Three simultaneous changes define the problem Sentra is facing: rapid deployment of co-pilots, assistants and cloud AI services; dissemination of sensitive data on cloud, SaaS and on-premise systems; and AI agents multiply as identities continuously accessing data at machine speed. The resulting fragmentation can leave security teams without a continuous picture of where sensitive records reside and which people or systems can reach them.

Sentra predicts that large organizations will go from a handful of agents to an exponential number in a matter of years, while only a small minority say they have governance that controls access to agent data. This changes the scope of access governance: permissions must be mapped to identities at machine speed along with employees and service accounts.

Co-founder and CEO Yoav Regev called AI agents the most active consumers of enterprise data and said many inherit access to information that hasn’t been fully classified or governed. Its alternative to relying solely on perimeter controls is to protect data where it resides and govern which human and AI identities can reach it before agents take action.

Data management before, during and after agent activity

Sentra’s approach starts with data at rest rather than inspecting each agent request as it occurs. The platform covers cloud services, SaaS applications, data warehouses, local file shares and knowledge repositories used by artificial intelligence. Before agents take action, it finds and classifies sensitive data, maps access for people, service accounts, and agents, including inherited permissions, enforces least privilege for non-human identities, and removes redundant, outdated, and high-risk data.

During an incident, data-driven detection alerts you to unusual access to sensitive information. Automated remediation can revoke access, remove risky sharing, and direct high-risk issues to existing data owners and workflows. Next, Sentra shows what information an agent could have accessed and provides business context on the affected datasets for security, legal and compliance teams.

The analysis is performed entirely within the customer’s environment, without moving sensitive data into the Sentra environment. Sentra says this project avoids the creation of a separate copied data estate and the retention, residency and compliance risks that can accompany it, a material consideration for regulated industries.

Fund product development and business expansion

Product investments will target broader visibility into agents, co-pilots and models and the sensitive data they touch, continuous least privilege for human and non-human identities, protection of data at rest, and rapid containment when an agent behaves unexpectedly. Sentra also plans to expand field and channel programs, increase brand and marketing activity and enter new geographies.

Chaim Sanders, Lyft’s CISO, said his organization adopted Sentra to support its growth, determine what data its agents can reach before activating it in its environment, and let security assist the implementation of AI rather than slow it down.

The round ties Sentra’s expansion to a specific control problem: reducing sensitive data exposure and unnecessary access before officers take action, while maintaining the means to contain and investigate incidents later. For companies deploying agents, the funding supports a model where data governance is established prior to agent activity rather than left to request-by-request inspection.