Enterprise AI Governance

Governance is either a delivery accelerator or a delivery killer. In enterprise AI, governance often becomes policy documents that don't map to how systems are built. For AI agents that retrieve internal data and can take action, governance must be operational: risk tiers, enforceable controls, audit logs, and clear accountability.

Start with risk classification. Not every use case is equal. Low-risk might be internal summarization with citations. Medium-risk might be drafting customer responses with mandatory review. High-risk might be write operations in systems of record or automated external communications. You don't ban high-risk use cases; you require stronger safeguards.

Permissions and data handling are governance mechanisms. Permission-aware retrieval prevents leakage. Least-privilege tool access prevents accidental automation sprawl. Audit logs create accountability. Evaluation gates ensure you don't expand scope until regression coverage exists. Rollout patterns (feature flags, staged launches, escalation paths) turn governance into a working system.