Best Practices for AI Identity Governance

AI identity governance is a comprehensive framework designed to manage and control the actions of AI agents, focusing on who they act for, what they can do, and under what circumstances these actions are authorized and recorded. It emphasizes the importance of distinguishing between agent identity security, which concerns authentication, and AI identity governance, which is centered on authorization and accountability. The framework introduces the concept of agentic identity, which combines the delegating human, workflow context, and declared intent to ensure actions are appropriately authorized. Key governance controls include explicit agentic identity, deny-by-default policies, layered authorization, policy-as-code, and zero standing credentials to prevent unauthorized access. Guardian Agents play a role in observing agent behavior to detect policy gaps and suggest improvements. Effective governance requires a real-time synchronization of policy data and the maintenance of a comprehensive audit trail that reconstructs the authority behind each action. The "model proposes; policy decides" approach separates probabilistic model reasoning from deterministic policy enforcement, ensuring that AI actions are rigorously scrutinized and authorized by established policies rather than relying solely on the AI model's judgment.