Sentra Raises $150M Series C to Expand Agentic Data Security Platform

Sentra announced on November 4, 2026, that Capital One Ventures led a $150 million strategic investment in the agentic data-security company. The Series C financing will expand Sentra’s platform for governing 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, Munich Re Ventures and Key1 Capital. A Capital One representative will also join Sentra’s board of directors. Sentra reported more than 300% year-over-year growth in the year preceding the announcement.

AI Agents Change the Data-Security Problem

Three simultaneous changes define the problem Sentra is addressing: rapid deployment of copilots, assistants and cloud AI services; sensitive data spreading across cloud, SaaS and on-premises systems; and AI agents multiplying as identities that access data continuously 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 expects large organizations to scale from a handful of agents to exponentially more within a few years, while only a small minority say they have governance controlling agent data access. That changes the scale of access governance: permissions must be mapped for machine-speed identities alongside employees and service accounts.

Co-founder and CEO Yoav Regev characterized AI agents as the busiest consumers of enterprise data and said many inherit access to information that has not been fully classified or governed. His alternative to relying solely on perimeter controls is to secure data where it resides and govern which human and AI identities can reach it before agents act.

Governing Data Before, During and After Agent Activity

Sentra’s approach starts with data at rest rather than inspection of every agent request as it happens. The platform covers cloud services, SaaS applications, data warehouses, on-premises file shares and knowledge stores used by AI. Before agents act, 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, obsolete and high-risk data.

During an incident, data-aware detection flags unusual access to sensitive information. Automated remediation can revoke access, remove risky sharing and route high-risk issues to data owners and existing workflows. Afterward, Sentra shows what information an agent could have reached and provides business context about the affected datasets for security, legal and compliance teams.

The analysis runs entirely inside the customer’s environment, without moving sensitive data into Sentra’s own environment. Sentra says this design avoids creating a separate copied data estate and the retention, residency and compliance risks that can accompany it, a material consideration for regulated industries.

Funding Product Development and Commercial Expansion

Product investment will target broader visibility into agents, copilots 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 scale field and channel programs, increase brand and marketing activity and enter new geographies.

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

The round ties Sentra’s expansion to a specific control problem: reducing sensitive-data exposure and unnecessary access before agents act, while retaining the means to contain and investigate incidents afterward. For enterprises deploying agents, the financing backs a model in which data governance is established ahead of agent activity rather than left to request-by-request inspection.