AI Governance: From Policy to Operating Model
Turning principles into enforceable controls, accountable roles, and auditable oversight.
Executive Summary
Enterprises must move beyond high-level AI principles toward enforceable controls and oversight. An operating model makes governance real by defining who is accountable, what artifacts exist, which controls are required, and how assurance is produced.
Governance Framing
AI governance should integrate with enterprise risk management, information security, legal/compliance, and internal audit. Treat AI as a system of controls and decision rights—not a set of recommendations.
Operating Model Building Blocks
- Roles & decision rights: RACI for use-case approval, model onboarding, exception handling, and incident escalation.
- Controls: Policy-to-control mapping (data handling, access, logging, evaluation, human accountability, third-party risk).
- Artifacts: Use-case registry, risk assessments, approvals, evaluation evidence, monitoring reports, and incident records.
- Oversight: Defined committee cadence, thresholds for board reporting, and measurable KPIs/KRIs.
Defensible Next Steps
- Inventory AI usage and establish a governed use-case registry.
- Define minimum control requirements by risk tier.
- Assign accountable owners and implement approval workflows.
- Publish required artifacts and evidence standards for auditability.
- Establish monitoring and an incident escalation path.
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