AI Governance Policy to Operating Model
A framework for turning AI principles and policies into use-case intake, risk classification, approval forums, controls, monitoring and executive oversight.
Updated 2026-07-07 / 9 min
The policy gap
AI principles are useful, but they rarely answer the operating question: who approves a use case, what evidence is required, how risk is classified, which controls apply and how monitoring is performed after deployment.
Use-case intake
Every AI use case should be described in operational terms: business workflow, affected users, data used, decision impact, human oversight, vendor dependency, security implications and expected control requirements.
Approval and controls
The operating model should define approval thresholds, risk categories, mandatory reviews, required controls, documentation expectations and escalation paths for higher-risk or unclear use cases.
Oversight after launch
Responsible AI does not end at approval. Executives need a review cadence for incidents, drift, complaints, human overrides, control failures, vendor changes and material changes to the use case.