Agentic AI Security: Visibility and Control for AI Agents at Work
Security teams have spent years tracking what employees do with data. The harder problem now is tracking what agents do on their behalf. AI agents, whether running in an IDE, installed locally on a laptop, or connected to internal data through a model context protocol (MCP) server, operate with the permissions of the user who deployed them. They read files, query databases, call external APIs, and generate outputs. And in most enterprise environments, security teams have no reliable way to see any of it.