AI agents can access data, use credentials, call tools and trigger workflows. Controls built for predictable applications and static identities may not be enough when an agent’s next action depends on its goal and context.
Securing Agentic AI in the Enterprise gives security and technology leaders a practical framework for governing that autonomy. Explore how to identify and own agents, limit their access, evaluate actions at runtime and preserve the evidence needed to understand what happened.
What you’ll learn
- Where agent risk emerges: Understand six threat vectors, from prompt injection and credential exposure to compromised context and multi-agent trust chains.
- Why identity alone is not enough: See how intent-aware authorization evaluates the action an agent wants to take, not just who the agent is.
- What a governed control flow looks like: Explore agent registration, short-lived credentials, mediated tool access, runtime decisions and audit evidence.
- Where to start: Use an assessment framework and phased roadmap to prioritize visibility, ownership, access governance and runtime controls.
The goal: Governed autonomy
Enterprises need agents that can act without operating beyond their intended boundaries. The whitepaper connects identity, access, supervision and auditability into one control model for scaling agentic AI with greater confidence.
Download the whitepaper
Get the framework for moving from fragmented agent visibility to accountable, intent-aware governance.
