The AI Governance Checklist

Seven principles and a five-step sequence for teams scaling with AI in their workflows.

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Pages 1 and 3 of the checklist

Every growing engineering team ends up running AI somewhere in its workflow, whether anyone signed off on it or not. The real question is not whether your team uses AI. It is whether you can still say, with confidence, who did what, which tools touched your data, and what it is costing you.

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The seven principles

Approve an AI stack, and make it the easy path

Fold AI activity into normal management visibility

Connect usage, cost and risk in one view

Separate what a person built from what AI generated

Build audit trails in by default

Control data before it reaches the model

Give autonomous agents runtime guardrails

And a five-step sequence for putting them in place

Take inventory

Classify the risk

Set policy boundaries

Build in visibility

Review and improve

Page 3 is a one page version you can pin up or tick off directly.