How to apply exposure management best practices to secure agentic AI

Govern agentic AI as an autonomous system

Agentic AI picks its own tools, pulls its own context, and acts without waiting for approval. Tenable research shows 70% of organizations have already integrated AI or Model Context Protocol (MCP) third-party packages, and 18% have granted AI services admin permissions that rarely get audited. Find out why AI security has to shift from protecting applications to governing systems.

Spot critical AI threats that traditional controls miss

Learn how to find shadow AI, prompt injection, and MCP risks in your environment. Explore the three threats that matter most and why traditional controls fail to detect them.

Uncover how safe capabilities can combine to create exposures

See how an agent that can read internal documents and also send emails can become an unintentional data exfiltration path, one that your existing security tools may not flag because nothing looks broken. Discover how to spot these toxic combinations before they become incidents.

Combat agentic AI security posture challenges with a framework and roadmap

Get an exposure management framework for agentic AI, from discovery down to runtime governance, and learn how to put it into practice by maintaining inventory, modeling attack paths, and continuously monitoring behavior. Plus, get a 180-day roadmap. Download the white paper to improve AI security posture and reduce exposure.