“We need an AI policy” is easy to say and hard to operationalize. Here’s what actually belongs in an enterprise AI governance framework, based on patterns we’ve seen work (and not work) across regulated and unregulated industries alike.
Model and data provenance
A working inventory of which models are in use, what data trained or fine-tuned them, and what data flows through them at inference time — without this, you can’t answer basic regulatory or customer questions about how an AI-assisted decision was made.
Human oversight for consequential decisions
Define, in writing, which categories of decisions require human review before acting on an AI system’s output — this is both a risk control and, increasingly, a regulatory requirement depending on your industry and jurisdiction.
Monitoring for drift and misuse
Production AI governance isn’t a one-time approval gate; it requires ongoing monitoring for model drift, unexpected use cases emerging from shadow IT, and prompt-injection-style misuse in customer-facing GenAI applications.
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