See Every Agent Before It Sees Your Data
AI agents now act continuously across endpoints, invoking tools and moving data without human review, and legacy EDR, DLP, and cloud-only tools cannot see any of it. This whitepaper lays out a three-pillar framework, visibility, observability, and controls, for governing agentic AI safely at the endpoint.
What's inside
Six Ways Agentic AI Expands Your Attack Surface
Shadow agents multiply fast: non-coding endpoint AI tools grew 43.2% in six months, often deployed with zero security review.
Agents don't authenticate or log activity the way people do, so there's no native audit trail connecting what they touched.
Access controls on the underlying data don't carry into the AI layer, so an agent can surface confidential information to users who shouldn't see it.
Why Legacy Security Tools Miss Agentic AI
EDR sees system behavior like file writes and network connections, not what data actually moved or where it went.
Legacy DLP fires on volume and content patterns, so it can't tell synthetic test data from production PII in the same channel.
Cloud and browser-extension tools are blind to agents running locally in IDEs, CLIs, and desktop automation frameworks.
A Three-Pillar Framework: Visibility, Observability, Controls
Visibility means continuously discovering and risk-scoring every agent, application, and MCP server, including ones running locally on endpoints.
Observability reconstructs the full execution lifecycle: what data an agent touched, which tools it called, and how each step connects.
Controls enforce guardrails at the moment of execution, coaching or redacting in context instead of relying on blunt, all-or-nothing blocking.