What is an AI governance checklist?

An AI governance checklist is a structured review of risks and controls before shipping an AI feature. It covers data and privacy, model behavior and output quality, security, human oversight, and launch readiness so teams do not discover gaps in production.

When should you run an AI governance review?

Run a governance review before any customer-facing launch, before processing sensitive or regulated data, and again when the model, data sources, or use case changes materially. Treat it as a gate in the release process, not a one-time paperwork exercise.

What belongs in responsible AI launch readiness?

Launch readiness includes documented owners, rollback plans, monitoring for quality and safety drift, user disclosure where required, and a path for humans to override or escalate bad outputs. If you cannot explain what happens when the model fails, you are not ready to ship.

AI Governance Checklist

Run a responsible AI review before you ship. Work through data and privacy, model behavior, security, human oversight, and launch readiness, track progress by section, and export a CSV for your records. Everything saves in your browser. No signup.

Overall progress

How I use this checklist before launch

I treat this as a release gate, not a checkbox exercise. Data and privacy covers consent, retention, PII handling, and whether training or inference data crosses policy lines. Model and output covers evals, hallucination risk, bias testing, and disclosure when users are talking to AI. Security covers access control, prompt injection, secrets in context, and vendor subprocessors. Human review covers escalation paths, override UX, and who owns incidents when the model fails.

Launch readiness is where program management earns its keep: named owners, rollback plan, monitoring dashboards, support runbooks, and executive sign-off where the blast radius is high. I export CSV after each review so audit and legal have a timestamped snapshot. Re-run the whole list when the model version, data source, or user population changes.

When to block the launch

Any unchecked item in data and privacy for regulated or customer PII workflows.
No human escalation path for high-stakes outputs.
No rollback plan or on-call owner for production incidents.
Eval coverage does not match the real prompts users will send.

Pair this with the AI use case prioritizer when scoping what to build next.

Built by Arsenii Samoilov, a Senior Technical Program Manager with 19+ years at Intuit, Atlassian, Adobe, Salesforce, Roku, and Apple. If your team needs help standing up program governance, get in touch.

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