Governance
Enterprise AI Governance Checklist
Practical controls for data ownership, model access, audit trails, and compliance.
Key points
- Security controls
- Audit readiness
- Role boundaries
Operational governance checklist
1. Data and model ownership clarity
Every AI data source needs a functional owner, technical owner, and lifecycle rules. Ownership ambiguity is a common root cause of compliance incidents and cross-team deadlocks.
Define model ownership in production as well: who approves changes, who validates quality, and who is accountable when outcomes drift.
2. Access control and role segregation
Not every profile should access prompts, sensitive datasets, or unanonymized outputs. Enforce role-based permissions by environment with least-privilege defaults.
Run periodic access reviews and automatic deprovisioning for stale accounts. This lowers risk exposure and makes audits substantially easier.
3. Traceability, auditability, and change control
Log who ran which model version, against which data, and with what output. Security and compliance teams should retrieve this evidence without engineering intervention.
Adopt risk-based change control. A sandbox prompt update is not equivalent to modifying a production classifier tied to financial or regulated decisions.
4. Continuous governance with lean routines
Governance is not a one-time policy deck. Run short periodic reviews of drift, critical errors, latency, and security alerts.
For out-of-threshold signals, define pre-approved actions such as rollback, human review gates, or temporary suspension to reduce incident impact.
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