Review of the AI policy

Annex A, Control A.2.4 in ISO/IEC 42001 is deceptively short, but auditors treat it as a governance heartbeat. A policy that is never revisited becomes shelfware, and a policy that is updated without control becomes a moving target employees cannot follow. The point of this requirement is to prove you manage AI governance as a living system: the AI policy stays aligned with how AI is actually built, bought, and used across the business, including by third parties.

For a CCO, GRC lead, or AI governance owner, operationalizing this requirement quickly means three things: (1) define a review cadence that is “planned” (scheduled, owned, and measurable), (2) execute the review with specific inputs that reflect real operational change, and (3) preserve evidence that the organization evaluated continued suitability and made accountable decisions (update, reaffirm, or retire). The practical goal is simple: when your AI footprint changes, your policy should keep pace without waiting for an audit finding or incident.

Regulatory text

Requirement (excerpt): “The organization shall review its AI policy at planned intervals.” ¹

What an operator must do

• Plan the interval: Define and document a recurring schedule for AI policy reviews, with ownership and governance touchpoints.
• Perform the review: Use consistent inputs (risk, inventory, incidents, third parties, exceptions) to evaluate whether the policy remains appropriate and effective.
• Record outcomes: Capture decisions, approvals, and actions, including policy updates or a documented reaffirmation that no changes are needed.
• Control changes: Publish the updated policy with version control, communication, and training impacts as applicable.

Plain-English interpretation

You need a repeatable process that periodically asks: “Does our AI policy still fit what we are doing with AI, and does it still control the risk we actually have?” Then you must prove you asked that question on schedule, that the right people answered it, and that the organization acted on the answer.

Auditors typically do not accept “we review it when needed” unless you can show a defined trigger mechanism and governance evidence that it happens consistently. “Planned intervals” means the review is on a calendar (or an equivalent formal cycle), assigned to a function or committee, and tracked like any other management system control.

Who it applies to

Entity scope

• Organizations developing AI systems (AI providers)
• Organizations deploying or using AI systems (AI users)
• Organizations doing both (common in practice, especially with embedded AI features and third-party AI tools)

Operational contexts that trigger scrutiny

• Centralized AI/ML teams building models
• Product teams shipping AI-enabled features
• Procurement onboarding AI-enabled third parties (including embedded AI in SaaS)
• Business functions using generative AI tools for decision support, customer interactions, marketing, HR, finance, or legal work
• Any environment with meaningful AI-related risk: customer impact, safety, discrimination risk, security exposure, data sensitivity, or regulated decisioning

What you actually need to do (step-by-step)

1) Assign ownership and governance path

• Name a policy owner (role-based, not person-based). Common owners: AI governance lead, CISO/GRC, or a cross-functional risk owner.
• Define approvers (e.g., AI governance committee, risk committee, or executive sponsor).
• Define required reviewers: Legal/Compliance, Security, Privacy, Data Governance, Model Risk (if applicable), and key product/business stakeholders.

Deliverable: AI Policy Review Procedure (can be a short SOP) that states owner, approvers, required reviewers, and review inputs.

2) Define “planned intervals” in a way you can execute

Pick an interval you can actually maintain. The standard does not prescribe frequency; it requires that the interval is planned and followed ¹.

Operationalize with:

• A recurring calendar event (committee agenda item) and/or a GRC workflow task.
• A tracking mechanism: policy register line item with next review date, last review date, version, and approval record.

Decision point: If your AI environment changes rapidly, add event-driven reviews (see Step 4) in addition to the baseline interval.

3) Define objective review criteria (so the review is not a “read-through”)

Create a checklist that forces a real assessment of suitability and effectiveness. Typical criteria:

• Scope alignment: Does the policy cover all AI uses in the organization, including shadow AI and third-party AI?
• Control alignment: Are policy requirements mapped to actual procedures (model development, procurement, monitoring, incident response)?
• Risk alignment: Do known risks and risk appetite statements match current AI deployments?
• Regulatory/contract alignment: Do customer commitments, internal standards, and contractual obligations require policy changes?
• Operational friction: Are teams requesting frequent exceptions? That’s a signal the policy may be unclear or unrealistic.

Tip from audits: A policy review that ends with “no changes” is acceptable if you document why and what inputs you considered.

4) Build a trigger list for off-cycle reviews

Planned intervals should be your minimum. Add triggers that force an interim review when reality changes, such as:

• New AI system class introduced (e.g., generative AI in customer-facing workflows)
• Material change in training data sources, model purpose, or decision automation
• Significant AI incident, near miss, or customer complaint
• New high-risk third party AI engagement or major change to an existing third party’s AI processing
• Repeated policy exceptions indicating gaps or impractical requirements

Deliverable: “AI Policy Review Triggers” section inside your SOP, plus a lightweight intake path (ticket, email alias, or governance forum).

5) Execute the review meeting/workflow with consistent inputs

Run a structured review with a packet that includes:

• Current AI policy (redline-ready)
• AI inventory summary (what AI exists, who owns it, and where it runs)
• Exceptions and approvals log
• Incident/issue log relevant to AI (security, privacy, model performance complaints)
• Third-party AI register summary (new/changed AI-enabled suppliers)
• Audit results or control testing outcomes tied to AI

If you don’t have these artifacts yet, capture what you can now and treat gaps as action items. A review can surface that your governance inputs are incomplete; document that and assign remediation owners.

6) Decide: update, reaffirm, or retire

Every review should produce one of three outcomes:

• Update: publish a new version with change summary and effective date.
• Reaffirm: document that the policy remains appropriate; record the evidence reviewed.
• Retire/merge: if AI policy content is duplicated elsewhere, rationalize policy structure and document the decision.

7) Communicate and operationalize changes

Policy updates that don’t reach operators fail in practice. Minimum actions:

• Notify impacted groups (engineering, product, procurement, customer support, HR, etc.).
• Update linked procedures, templates, and training materials.
• Confirm that tooling/workflows reflect new requirements (e.g., procurement intake questions, model release checklists).

Where Daydream fits naturally: If you manage many AI-enabled third parties, Daydream can centralize the third-party inventory, exceptions, and review tasks that feed the policy review packet, so policy decisions match your supplier reality and don’t depend on scattered spreadsheets.

Required evidence and artifacts to retain

Auditors typically want evidence of planning, execution, and change control:

Planning

• AI policy document with version history and next review date
• AI Policy Review SOP/procedure
• Governance calendar or GRC task schedule showing planned intervals

Execution

• Meeting agenda/minutes or workflow record of the review
• Review packet (inputs listed above) or a checklist showing what was evaluated
• Attendance/approvals (names, roles, dates)

Outcomes

• Approved redline and final policy version
• Decision log: update vs reaffirm, plus rationale
• Communication record (announcements, attestations where applicable)
• Linked updates to procedures/templates/training (or action items with owners)

Common exam/audit questions and hangups

• “Show me the last two AI policy reviews and the evidence you followed the schedule.”
• “Who owns the AI policy, and who approves changes?”
• “What inputs do you review to judge whether the policy is effective?”
• “How do third-party AI tools get captured in your policy scope?”
• “If you had an AI-related issue, how would that trigger an off-cycle review?”
• “Where do you track exceptions, and how do exceptions influence policy updates?”

Hangup areas:

• Reviews happen informally but are not documented.
• The policy is reviewed, but supporting procedures are not updated, so the organization cannot comply with the policy as written.
• Third-party AI usage is excluded from policy scope, even though it drives real risk.

Frequent implementation mistakes and how to avoid them

1. Mistake: “Planned intervals” defined but not tracked.
   Avoid it: Maintain a policy register with last review date, next review date, owner, and approver. Treat overdue reviews as an audit issue, not an admin task.

2. Mistake: Reviews are a legal edit, not a governance check.
   Avoid it: Require operational inputs (inventory, incidents, exceptions, third-party changes). Make those inputs mandatory in the SOP.

3. Mistake: No change control discipline.
   Avoid it: Version control, approval records, and a change summary are non-negotiable. If your policy lives in a wiki, preserve immutable approval evidence elsewhere.

4. Mistake: The policy expands faster than operations.
   Avoid it: If you add requirements, confirm there is an implemented procedure, owner, and system/workflow to support them. Otherwise you create internal noncompliance by design.

Enforcement context and risk implications

No public enforcement cases were provided for this requirement in the supplied sources. Practically, the risk is governance breakdown: outdated policy leads to inconsistent controls, unmanaged third-party AI use, weak incident response alignment, and findings during ISO/IEC 42001 certification audits. The control is also an anchor for demonstrating management system maturity: you can show that AI governance is monitored and updated, not static ¹.

Practical 30/60/90-day execution plan

First 30 days (stabilize and schedule)

• Assign AI policy owner and approver group; document in an SOP.
• Set the planned interval and create the recurring governance calendar item.
• Stand up a simple policy register entry with versioning and next review date.
• Build the review checklist and minimum review packet template.

Days 31–60 (run the first review “for real”)

• Gather inputs: AI inventory snapshot, third-party AI list, exceptions, incidents/issues.
• Hold the review meeting/workflow; document attendance, decisions, and action items.
• Decide update vs reaffirm; if update, draft redlines and route approvals.
• Define off-cycle triggers and a channel to submit triggers.

Days 61–90 (make it repeatable and auditable)

• Publish updated policy (or reaffirmation memo) with version history and change summary.
• Update dependent procedures/templates and communicate changes to operators.
• Create a lightweight dashboard: next review date, open actions, exception trends.
• Test evidence quality: can you produce the full trail in one packet for an auditor?

Frequently Asked Questions

What counts as a “planned interval” under ISO/IEC 42001?

An interval is “planned” when it is defined in advance, owned, scheduled, and tracked as part of governance ¹. Auditors expect a repeatable cadence, not ad hoc updates.

Do we have to change the AI policy every time we review it?

No. You can reaffirm the policy if you document the inputs reviewed and why the policy remains appropriate. Keep the approval record and rationale as audit evidence.

Who should approve the AI policy review outcome?

Use a documented approver group that matches your governance model, often a cross-functional risk or AI governance forum. The key is consistent authority and a recorded decision trail.

How do we include third-party AI in the policy review?

Include a summary of AI-enabled third parties and material changes as a required review input. If procurement cannot produce that summary, treat it as a governance gap and assign an action item.

What evidence is most likely to fail an audit?

Missing proof that reviews happened on schedule, and missing proof of who approved the outcome. Informal email threads without clear decision records tend to create audit friction.

We have multiple AI policies across regions. Can we review them together?

Yes, if you document the scope of the review, cover each applicable policy/version, and record outcomes per policy. Auditors need traceability from each governed document to its review and approval.

Footnotes

1. ISO/IEC 42001:2023 Artificial intelligence — Management system