AI Governance

The policies, processes, and organizational structures that oversee how AI agents are built, deployed, monitored, and retired. Governance covers model selection approval, data access policies, audit logging, bias testing, incident response, and accountability assignment. As agents take more real-world actions (sending emails, modifying records, spending budget), governance frameworks ensure those actions are authorized, traceable, and reversible. Governance starts with visibility: you can't oversee agents you don't know are running, so a Shadow AI discovery process and a central registry are prerequisites, not afterthoughts.