AI Governance Checklist for Enterprises (2026)

An enterprise AI governance checklist should cover accountability, system inventory, risk classification, data and vendor controls, human oversight, evaluation, monitoring, incidents, change management, records, and retirement. This 34-control version maps practical evidence to Govern, Map, Measure, Manage, and EU readiness. It is a working template—not legal advice or a compliance certificate.

Download editable CSV

Use rule: do not mark a control complete because a policy mentions it. Record the accountable owner, the minimum evidence, the review date, and the condition that will trigger reassessment.

The checklist

Govern: accountability and operating structure.

Governance starts with decision rights, a complete inventory, and evidence ownership. A central AI committee can coordinate the system, but it cannot replace accountable business, risk, data, security, and technology owners.

Govern

7 controls

Executive accountability: every AI system has a named accountable executive and business owner.
Policy: an approved AI policy defines permitted, restricted, and prohibited activity.
Inventory: systems, models, vendors, owners, purpose, users, data, and status are recorded.
Risk tiering: a repeatable classification method determines review depth and approval rights.
Decision rights: approval, exception, escalation, suspension, and retirement authority are documented.
AI literacy: role-based training matches employees’ authority and exposure.
Third parties: procurement and contracts require relevant AI, data, audit, change, and exit evidence.

Map

7 controls

Purpose: intended outcome, users, affected decisions, and prohibited uses are approved.
Stakeholders: affected people, operators, reviewers, and escalation recipients are identified.
Data: sources, lineage, rights, quality, retention, sensitivity, and residency are mapped.
Obligations: applicable laws, regulations, contracts, policies, and sector rules are recorded.
Misuse: foreseeable misuse, failure modes, affected groups, and downstream effects are documented.
Human rights: review, override, challenge, appeal, and final decision rights are explicit.
Baseline: process cost, quality, risk, volume, and exception rates exist before deployment.

Measure

8 controls

Acceptance tests: task, quality, cost, risk, and reliability thresholds are set before deployment.
Task performance: accuracy and end-to-end task success are tested on representative cases.
Fairness: bias and disparate impact are evaluated when relevant to the use and population.
Privacy and security: testing covers access, leakage, injection, misuse, and sensitive data.
Adversarial testing: misuse and red-team scenarios match the system’s risk and exposure.
Oversight: human reviewers can detect, understand, override, and escalate failures in practice.
Traceability: model, prompt, data, vendor, evaluation, and decision versions can be reconstructed.
Business evidence: outcomes are measured against a signed baseline by a named metric owner.

Manage

8 controls

Approval: residual risk is documented and accepted by the authorized owner before go-live.
Monitoring: production thresholds, alerts, reviewers, and response times are operational.
Incidents: response, containment, notification, rollback, and kill-switch procedures are tested.
Change control: model, prompt, data, vendor, integration, and use changes trigger review.
Challenge: complaints, corrections, appeals, and adverse outcomes reach an accountable human.
Review and retirement: periodic review, stop conditions, and retirement criteria are scheduled.
Vendor resilience: performance, concentration, portability, continuity, and exit are monitored.
Records: approvals, tests, changes, incidents, outcomes, and reviews are retained and retrievable.

EU AI Act readiness

4 prompts

Role and classification: provider, deployer, importer, distributor, GPAI, and risk status are assessed.
Transparency: notices and AI-generated-content controls are mapped to current requirements.
High-risk duties: applicable controls, documentation, monitoring, oversight, and timelines are mapped.
Supply chain: vendor and GPAI evidence, contracts, interfaces, and responsibilities are documented.

Evidence model

What “complete” means for each control.

A useful control has an owner, evidence, a decision threshold, and a review trigger. The exact artifact varies by company and risk level, but completion should be independently reviewable.

Field	Required question	Example evidence
Owner	Who has authority and is answerable for this control?	Named role in an approved RACI or decision record
Evidence	What artifact proves the control operates?	System register, test result, approval, log, incident exercise, or review record
Threshold	What result passes, escalates, pauses, or stops deployment?	Task-quality minimum, risk limit, response time, cost ceiling, or override rate
Trigger	What change requires reassessment?	New model, prompt, data source, vendor version, workflow, population, or jurisdiction
Date	When was the evidence last reviewed?	Dated approval plus next scheduled or event-driven review

Primary sources

Frameworks used to structure the checklist.

This is an original operational checklist informed by official sources. It does not reproduce any framework in full and should be tailored with current sector, jurisdiction, security, privacy, and legal requirements.

NIST AI RMF PlaybookOfficial voluntary guidance organized around Govern, Map, Measure, and Manage; NIST notes that the Playbook is not a universal ordered checklist.
European Commission: AI ActOfficial overview of the risk-based framework, obligations, implementation timeline, and current support instruments.
OECD AI PrinciplesOfficial principles covering human rights and fairness, transparency, robustness, security, safety, and accountability.
NIST AI 600-1: Generative AI ProfileCross-sectoral companion resource for applying the AI RMF to generative AI risks.

Legal boundary: this page is operational guidance, not legal advice. Official requirements and dates can change. Confirm the current rules and applicability with qualified counsel before relying on this checklist for compliance.

FAQ

AI governance checklist questions.

What should an enterprise AI governance checklist include?

An enterprise AI governance checklist should cover executive accountability, a system inventory, risk classification, data and vendor controls, human oversight, testing, monitoring, incident response, change control, records, and retirement. Each control needs a named owner and minimum evidence; a policy without operating evidence is not a complete governance system.

How does this checklist relate to the NIST AI RMF?

This checklist organizes practical evidence around the NIST AI RMF functions Govern, Map, Measure, and Manage. NIST describes its Playbook as voluntary guidance, not a universal ordered checklist. Companies should tailor these controls to the use case, sector, jurisdiction, risk appetite, existing control environment, and the current version of official guidance.

Does the checklist make a company compliant with the EU AI Act?

No. A checklist cannot establish EU AI Act compliance. The company must determine its role, system classification, applicable dates, jurisdiction, and specific obligations using current official guidance and qualified legal advice. This resource adds four EU-readiness prompts so teams can identify missing analysis, documentation, transparency controls, and supply-chain evidence before deployment.

Who should own AI governance in a company?

Executive accountability should be explicit, while control ownership remains distributed. Business owners define purpose and outcomes; risk, legal, privacy, security, data, technology, procurement, and operations own relevant controls. One governance lead should maintain the system register and review cadence, but should not silently inherit every operational or legal decision.

How often should AI governance controls be reviewed?

Review frequency should follow risk and change, not an arbitrary annual calendar. Reassess when the model, prompt, data, vendor, intended use, affected population, jurisdiction, or workflow changes, and after incidents. High-impact systems need more frequent monitoring. Record the review date, evidence, decision, residual risk, owner, and next trigger.

About Paul Okhrem

Paul Okhrem is an AI Transformation Consultant and Fractional Chief AI Officer. His governance work connects decision rights, operating controls, implementation evidence, and business measurement.

Full biography · LinkedIn · Crunchbase

AI governance consulting AI governance statistics All research & tools

The original wording and structure of this checklist are licensed under CC BY 4.0 with attribution to Paul Okhrem. Third-party sources retain their own terms.