Practical template · Updated July 23, 2026
AI Governance Checklist
Turn AI policy into operating evidence. This checklist gives each use case a purpose, accountable owners, release gates, human control, monitoring, incident response, and a real stop path.
How to use it
A checkbox is not evidence
Record the owner, proof, decision, and next review.
Use one copy for one bounded AI workflow. For every item, identify who owns the answer, what evidence supports it, who approved the residual risk, and when the control will be checked again.
Choose one use case
Evaluate a specific workflow or decision—not “AI” in general.
Attach evidence
Link each answer to a test, record, owner, approval, or operating signal.
Stop on material gaps
High-consequence unknowns are review gates, not boxes to mark complete.
Set the next review
Controls age as models, data, vendors, people, and conditions change.
Working checklist
Eight control areas · Thirty-two checks
Move from purpose to evidence, operation, and correction.
Purpose, scope, and risk
A bounded use case with a clear benefit, consequence level, and decision boundary.
AI inventory and accountability
Every AI system and material feature has a named owner and a current record.
Data, privacy, and records
Only necessary, permitted, protected, and fit-for-purpose data enters the workflow.
Security, access, and change control
The system operates inside explicit access, action, supplier, and release boundaries.
Human oversight, transparency, and recourse
People can understand AI involvement, exercise judgment, and correct consequential outcomes.
Testing and acceptance evidence
Predefined evidence shows where the system works, where it fails, and whether it may launch.
Monitoring and incident response
The organization can detect change, contain harm, investigate, recover, and learn.
People, training, and review cadence
People know the rules, limits, escalation route, and when the system must be reviewed again.
Minimum launch gate
Ten questions before real-world use
Do not launch on optimism where evidence is required.
A “no” does not always end the project. It means the gap needs a named owner, a documented decision, and a control proportionate to the consequence before the workflow reaches people.
- 01
The use case, affected people, expected benefit, and unacceptable outcomes are documented.
YesNoNot applicable - 02
The consequence level and allowed automation level have been approved.
YesNoNot applicable - 03
Every business, domain, technical, review, escalation, and stop role has a named owner.
YesNoNot applicable - 04
The system, model, provider, data sources, integrations, and downstream actions are inventoried.
YesNoNot applicable - 05
Data permission, privacy, retention, access, and supplier handling have been reviewed.
YesNoNot applicable - 06
Security threats, access limits, tool permissions, rollback, and vendor risk have been tested.
YesNoNot applicable - 07
Human review happens before consequential action and the reviewer can genuinely disagree.
YesNoNot applicable - 08
Acceptance thresholds were set in advance and the system passed realistic and adversarial tests.
YesNoNot applicable - 09
Users have appropriate notice, support, correction, appeal, and incident-reporting paths.
YesNoNot applicable - 10
Monitoring, reapproval, incident response, pause, retirement, and evidence-retention rules are active.
YesNoNot applicable
Review cadence
Governance is an operating cycle
Reopen the decision when the system or its context changes.
Define purpose, risk, people affected, owners, data, suppliers, and prohibited uses.
Use-case brief and accountable approval
Complete testing, security and privacy review, reviewer training, monitoring, rollback, and user communication.
Signed launch decision with unresolved limits
Review outcomes, overrides, complaints, incidents, drift, access, suppliers, cost, and control performance.
Dated operating review and action log
Reassess purpose, model, data, prompts, integrations, permissions, population, vendors, and consequence.
Reapproval, rollback, or retirement decision
Reference alignment
Ground the checklist in recognized public guidance
Use this operating layer alongside—not instead of—formal requirements.
NIST AI Risk Management Framework
NIST organizes AI risk work through Govern, Map, Measure, and Manage. The checklist converts those functions into use-case owners, evidence, gates, and recurring decisions.
Review NIST AI RMF Review the NIST playbookOECD AI Principles
The OECD principles address human rights and democratic values, transparency, robustness, safety, accountability, and inclusive benefit. They help teams test the broader purpose behind a control.
Review the OECD AI PrinciplesHuman-centered AI controls
The Institute’s framework explains the purpose, ownership, meaningful human control, evidence, monitoring, and redress concepts that this checklist turns into an operating record.
Use the human-centered AI framework Review institutional standardsReferences indicate source alignment only. They do not imply affiliation, endorsement, certification, or approval by NIST, OECD, or any other organization.
Common questions
AI governance checklist FAQ
Know what the checklist can—and cannot—prove.
What should an AI governance checklist include?
A useful checklist connects purpose and risk to named owners, an AI inventory, data and privacy rules, security boundaries, meaningful human oversight, acceptance testing, user transparency, monitoring, incident response, training, change control, and retirement. Each control should identify the evidence that proves it is operating.
Who should own AI governance?
Executive leadership should assign accountability, but no single department can operate the whole system. Business, domain, technical, security, privacy, legal or compliance, operations, and affected-user perspectives have different responsibilities. Every use case still needs one named owner with authority to pause or stop it.
How often should an AI governance checklist be reviewed?
Review before approval, before launch, on a defined operating cadence, after an incident, and after a material change to purpose, model, data, prompts, integrations, permissions, population, or supplier. Higher-consequence systems generally need more frequent review and tighter escalation thresholds.
Is an AI governance checklist the same as an AI audit?
No. A checklist helps a team design and operate controls. An audit independently evaluates whether required controls exist, work as intended, and have reliable evidence. A completed checkbox without evidence or independent challenge is not an audit result.
Can I download this AI governance checklist as a PDF?
Yes. Use the Print or save as PDF button and select your browser’s PDF destination. The page is formatted for printing and leaves the checkboxes, owner fields, evidence prompts, and launch gate visible.
Does completing this checklist prove legal or regulatory compliance?
No. This is general educational guidance, not a certification, legal opinion, regulatory approval, or substitute for review of a specific system, jurisdiction, industry, contract, or affected population. Use qualified professional review where the use case requires it.
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