Governance Objective
Use this checklist to deploy AI with clear ownership, policy controls, and launch gates while keeping delivery velocity high.
- Define accountable owners for model risk, data risk, and operational risk.
- Align compliance requirements with real deployment workflows.
- Prevent uncontrolled model behavior in customer-facing decisions.
Control Design Checklist
- Data controls: lineage, consent basis, retention policy, and access boundaries.
- Model controls: versioning, validation thresholds, bias testing, rollback plans.
- Access controls: role-based permissions, approval logs, and environment separation.
- Decision controls: human override, escalation paths, and audit evidence.
Risk Tiering Model
Classify each AI use case before launch.
- Tier 1 (Low): internal productivity assistance; light review cadence.
- Tier 2 (Medium): customer communication or pricing support; monthly governance review.
- Tier 3 (High): policy-sensitive or regulated outcomes; mandatory human approval and stricter evidence.
Operational Launch Gates
- Pre-launch: control evidence complete and owner sign-off documented.
- Pilot launch: monitor drift, exception rates, and override frequency daily.
- Scale launch: expand only after risk and performance metrics stay in bounds for 30 days.
FAQ
- Who should own AI governance?
Ownership should be federated: product owns use-case outcomes, data owns controls, and a governance lead coordinates sign-offs and evidence.
- Will governance slow delivery?
Not if controls are embedded into release workflows and tiered by risk. The goal is predictable delivery, not heavy process.
- How often should controls be reviewed?
Review high-risk systems monthly and all systems quarterly, with immediate review after major model or policy changes.