AI Agent Governance
AI agent governance is the discipline of managing how AI agents identify themselves, what they can access, and how they behave when acting autonomously on behalf of humans. It covers the policies, enforcement mechanisms, and audit trails that ensure agents operate within boundaries set by the organization, not just the user who triggered them.
Why It Matters
AI agents are no longer experimental. They run in production, access company systems, and take actions that affect real data. But the governance infrastructure built over the past two decades assumes a human is in the loop. Approval workflows, access reviews, compliance audits: all designed for people who read prompts and click buttons.
Agents break that assumption. They act faster than humans can review, chain together multiple system calls in a single task, and inherit permissions through delegation rather than direct assignment. When an agent accesses your company's Slack, GitHub, and database in one session, the risk is not any single action. It is the accumulated access path.
Without governance, organizations face two failure modes. The first is shadow AI: employees connecting agents to company systems without IT awareness. The second is over-permissioned agents: authorized agents that hold more access than any single task requires. Both create audit gaps that existing compliance frameworks (ISO 27001, SOC 2, FCA, NIS2) are not designed to catch.