AI Governance Checklist for Enterprises

Most organizations that say they have AI governance have a policy document. Governance that works is a set of structures and controls, each producing evidence: a register, owners, tiers, evaluation gates, oversight, audit trails, vendor management, incident response, and a review cadence. This checklist covers them. It is the operational form of the agentic AI governance whitepaper and complements ai governance framework and ai governance for enterprises. This is general guidance, not legal advice.

Who should use this checklist?

Executives sponsoring AI, risk and compliance leaders, security teams, and engineering leaders establishing or auditing an AI governance program.

Is there a register?

1. Every AI system in production or pilot is listed with purpose, owner, risk tier, autonomy level, systems and data touched, evaluation status, and review date.
2. Systems not in the register cannot hold credentials or reach production.
3. The register includes vendor-provided AI and embedded features, not only in-house builds.
4. The register is maintained by a named function and reviewed monthly.

Reference: how to build a model registry.

Is ownership named?

1. Each system has a named business owner accountable for behavior and outcomes.
2. Each system has a named engineering owner accountable for reliability, cost, and evaluation.
3. A governance body with executive sponsorship spans engineering, risk, security, legal, privacy, and business owners.
4. Decision rights for approvals, autonomy changes, and incident response are documented.

Reference: ai governance board.

Are policies and standards in place?

Policy or standard	Contents
Acceptable use	What employees may and may not do with AI tools and data
Risk tiering	Consequence-based tiers and the controls each requires
Development standard	Specification, evaluation, safety testing, documentation requirements
Oversight standard	Gate policies by consequence; queue staffing; autonomy levels
Data handling	Classification, permissions, provider terms, retention, residency
Vendor and model-provider standard	Due diligence, terms, change control, exit
Incident response	Containment, reconstruction, remediation, disclosure
Training	Role-based training for users, reviewers, owners, and engineers

Reference: ai policy template and ai acceptable use policy.

Is risk tiered by consequence?

1. Each system is tiered on what it can affect, reversibility, data sensitivity, and regulatory exposure.
2. Controls scale with tier: documentation, validation independence, gate requirements, monitoring depth.
3. Approval authority for launch and autonomy changes is defined per tier.
4. Tier assignments are reviewed when scope or capability changes.

Reference: ai risk register and ai risk management.

Is evaluation required and evidenced?

1. Systems have a specification with acceptance criteria before build.
2. A golden dataset and safety suite run in CI and gate release.
3. Higher tiers receive independent validation.
4. Fairness testing applies where outcomes affect people in regulated or sensitive ways.
5. Production monitoring covers quality, drift, cost, and safety signals.
6. Provider model changes trigger re-evaluation.

Reference: the AI evaluation and testing whitepaper and how to build an ai quality gate.

Is human oversight real?

1. Approval gates on consequential actions are enforced in code.
2. Review queues are staffed, with SLAs and context.
3. Reviewers have authority to reject and to trigger autonomy reductions.
4. Autonomy levels are recorded with evidence criteria for changes.
5. Gate metrics are monitored for ceremonial review.

Reference: how to build a human review queue.

Are decisions auditable?

1. Every system logs inputs, context, model versions, outputs, actions, and approvals in an append-only trail.
2. Redaction and access control protect the trail.
3. Retention matches regulatory requirements.
4. Reconstruction of any decision has been tested.

Reference: how to build an ai audit trail.

Are vendors and providers managed?

1. AI vendors and model providers are in the third-party risk program.
2. Due diligence covers method, security, data terms, and stability.
3. Contracts cover data use, IP, change notification, and exit.
4. Concentration risk is assessed; fallbacks exist.
5. Provider changes are tracked and re-validated.

Reference: ai third-party risk management and the ai vendor evaluation checklist.

Is incident response ready?

1. An AI incident playbook exists with containment (autonomy drop, credential revocation), reconstruction, remediation, root cause, and prevention.
2. Disclosure obligations are mapped.
3. The playbook has been exercised.
4. Incidents feed the evaluation suites and the register.

Reference: ai incident response checklist.

Is the cadence running?

1. Per change: evaluation gates.
2. Weekly: owners review quality samples and queue metrics.
3. Monthly: governance body reviews register, autonomy changes, incidents, cost, drift.
4. Quarterly: permissions re-certified; specifications reviewed; red-team exercises on high tiers.
5. On trigger: model, vendor, security, and regulatory changes.

Reference: how to run an ai steering committee.

How should gaps be prioritized?

Register and ownership gaps first, because nothing else attaches without them. Then evaluation gates and oversight for the highest tiers. Then audit trails, vendor management, and incident response. Policies are written alongside, expressed as the standards these controls implement rather than as aspirations.

How FISTA Solutions supports AI governance

FISTA Solutions builds the engineering side of governance into every system it delivers: specifications, evaluation gates, consequence-based gates, audit trails, and observability, and it helps establish the register, tiering, and cadence around them. The AI enablement practice provides the shared platform that makes controls consistent, AI agents are delivered governed by default, and forward deployed engineers work with your risk, security, and legal functions. The record behind the approach is 150+ projects with 99.9% uptime.

To assess your AI governance against this checklist, message FISTA on WhatsApp, or read nist ai risk management framework explained for the public framework alignment.