Governing enterprise AI agents requires three distinct control layers across the agent lifecycle. Build-time governance ensures agents are constructed securely through code reviews, dependency scanning, model allowlists, and secrets management. Deployment-time governance controls how agent instances are configured — covering tool permissions, data access, action limits, and tenant isolation — introducing the concept of Agent Posture Management analogous to CSPM. Runtime governance enforces safe behavior during live operation by detecting prompt injection, data leakage, unsafe tool calls, and malicious inputs in real time. Both deployment and runtime layers are necessary: misconfiguration creates structural risks that runtime enforcement alone cannot fix, while even well-configured agents can encounter dynamic threats at runtime. Enterprises scaling to hundreds or thousands of agents need all three layers working together.
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Emerging Governance ChallengesLayer 1: Build-Time Governance — Controlling How Agents Are CreatedLayer 2: Deployment-Time Governance — Controlling Agent Configuration and PostureLayer 3: Runtime Enforcement Governance — Controlling What Agents Actually DoDeployment Governance and Runtime Governance Are Equally ImportantA Simple Way to Think About Agent GovernanceSort: