AI Governance & Compliance
AI governance sets organizational policy for what models and data can be used, by whom, and under what oversight.
Overview
Beyond technical controls, an organization deploying AI needs policy-level answers: which models are approved for use, what data may be sent to them, who can approve a new AI use case, and how that’s all documented for audit or regulatory purposes.
Where It Fits
Governance Policy
Use Case Review
Approved Models/Data
Deployed Feature
Key Points
- Approved model/vendor list
- Many organizations restrict which model providers or model families may be used, based on data handling and compliance terms.
- Data classification policy
- Governance typically defines what categories of data may or may not be sent to a third-party model provider.
- Audit trail
- Regulatory and internal audit needs usually require documenting which AI systems exist, what they do, and how they were reviewed.
Interview Question
What does “AI governance” actually mean in an enterprise setting, beyond general security practices?
It’s the organizational policy layer above individual technical controls — which models and providers are approved, what data classifications may be sent externally, who signs off on a new AI use case, and how all of that is documented for audit or regulatory review. Security controls implement the policy; governance decides what the policy should be.
Explain It in 30 Seconds
AI governance sets organizational policy for AI use — approved models and providers, data handling rules, and an audit trail for review — as the policy layer that technical security controls then implement.