AI Governance
The framework of policies, controls, and audits that govern how AI is built and used in an organization.
AI governance covers risk assessment, data-handling policy, model approval workflows, bias testing, audit trails, and incident response. It translates regulations like the EU AI Act, ISO 42001, and the NIST AI RMF into concrete engineering requirements.
For most enterprises, governance is not a blocker — it is a checklist of things that should already exist: an inventory of AI use cases, a review process for high-risk applications, logs of model inputs and outputs, and a way to explain decisions when regulators ask.
Governance failures — a chatbot giving legal advice, a hiring model discriminating, a summarizer leaking PII — are almost always process failures, not model failures.
Key points
- Maintain an inventory of every AI use case and its risk tier
- Log all model inputs and outputs for high-stakes systems
- Bias and safety evaluations should run on every model or prompt change
- Human review is mandatory for irreversible or high-impact actions
Common use cases
Frequently asked
Related terms
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