AI Policy Review Process: Keeping Your Policy Current

Your AI policy was carefully crafted—stakeholders consulted, legal reviewed, board approved. Then it sat in a SharePoint folder, unchanged, while the AI landscape transformed around it. ChatGPT launched. Your industry got new guidance. Employees started using tools the policy never contemplated.

AI policies need regular review. Technology changes fast, regulations evolve, and business needs shift. This guide shows you how to keep your AI policy current and relevant.

Executive Summary

• AI policies require regular review because the AI landscape changes faster than most policy areas
• Recommended review cadence: annual formal review, plus triggered reviews for significant changes
• Review triggers: new technology, new regulation, incidents, business changes, stakeholder feedback
• Review process should be documented, consistent, and involve appropriate stakeholders
• Communication of changes is as important as making them—updated policies must reach affected people
• Version control enables tracking what changed and when

Why This Matters Now

The AI landscape is changing rapidly. Policies written before generative AI may have significant gaps. Policies written for early GenAI may not address agentic AI or multimodal capabilities emerging now.

Regulations are evolving continuously. What was guidance becomes law. What was unregulated becomes regulated. Policies must keep pace.

Practices drift from policies. Without review, actual AI usage diverges from what policies permit or prohibit. Gaps create risk.

Shadow AI emerges where policies have gaps. When policies don't address new tools, people use them anyway—often without appropriate governance.

Definitions and Scope

Scheduled vs. Triggered Reviews

Scheduled reviews: Regular reviews on a fixed calendar (annual, semi-annual). Ensure policies are current even if nothing specific triggers a review.

Triggered reviews: Reviews initiated by specific events—new technology, incident, regulatory change. Address emerging issues that can't wait for scheduled reviews.

Both types are needed. Scheduled reviews catch gradual drift; triggered reviews address acute changes.

Minor Updates vs. Major Revisions

Minor updates: Clarifications, corrections, terminology updates. Don't change policy intent. May not require full approval cycle.

Major revisions: Significant changes to scope, requirements, or obligations. Require full stakeholder input and approval.

Distinguish between them in your process—major revisions need more rigor.

Policy vs. Procedure Updates

Policy: High-level requirements and principles. Changes less frequently. Typically requires senior approval.

Procedure: Detailed implementation guidance. Changes more often. May have delegated approval authority.

Some organizations maintain separate documents; others combine them. Know your structure.

When to Review: Triggers

Technology Triggers

New AI capabilities:

• Major new AI releases (e.g., new foundation models)
• New categories of AI tools (e.g., autonomous agents)
• AI features added to commonly used tools
• Significant capability improvements in existing tools

Action: Review whether current policy addresses new capabilities; update guidance if gaps exist.

Regulatory Triggers

Regulatory changes:

• New AI legislation or regulations enacted
• New guidance from regulators
• Enforcement actions that clarify expectations
• Updates to industry standards

Action: Review policy compliance with new requirements; update as needed.

Incident Triggers

Internal incidents:

• AI-related security incidents
• AI-related compliance failures
• Near-misses that revealed policy gaps
• Employee complaints or confusion about AI policy

External incidents:

• High-profile AI incidents at other organizations
• Industry peer incidents in similar contexts

Action: Review whether incident reveals policy gaps; update to prevent recurrence.

Business Triggers

Business changes:

• New products or services using AI
• Entering new markets or jurisdictions
• Significant changes to AI vendor relationships
• Organizational restructuring affecting governance

Action: Review whether policy addresses new business context; update scope if needed.

Feedback Triggers

Stakeholder input:

• Accumulated questions about policy interpretation
• Requests for clarification or change
• Governance committee recommendations
• Audit findings or recommendations

Action: Review whether feedback indicates systemic issues; update policy to address.

Step-by-Step Review Process

Phase 1: Establish Review Calendar (Initial Setup)

Set up your regular review schedule.

Annual review (recommended minimum):

• Schedule date in advance (same month each year)
• Assign responsibility
• Include in governance calendar

Trigger monitoring:

• Assign someone to watch for triggers
• Define what triggers a review
• Document escalation path when trigger occurs

Phase 2: Trigger Assessment (When Event Occurs)

When a potential trigger occurs, assess whether review is needed.

Assessment questions:

• Does this affect our AI policy?
• Is the impact significant enough to warrant review?
• Can this wait for scheduled review, or is urgency required?

Decision:

• No review needed → document rationale
• Include in next scheduled review → add to review agenda
• Immediate review required → initiate triggered review process

Phase 3: Prepare for Review

Gather information needed for effective review.

For scheduled reviews:

• Compile trigger events since last review
• Gather accumulated questions and feedback
• Inventory new AI usage and tools
• Review regulatory developments
• Assess current compliance and gaps

For triggered reviews:

• Document the trigger event
• Assess impact on current policy
• Identify specific sections affected
• Prepare recommended changes

Phase 4: Gather Stakeholder Input

Policy changes should reflect diverse perspectives.

Stakeholders to consult:

• Legal/compliance (regulatory requirements)
• IT/security (technical implications)
• HR (employee implications)
• Business units (practical implications)
• Risk management (risk implications)
• Employees (practical feedback)

Input methods:

• Review committee meeting
• Written comment period
• Targeted consultations
• Employee surveys

Phase 5: Draft Updates

Prepare proposed policy changes.

For each change:

• Document current language
• Propose new language
• Explain rationale for change
• Note who requested or affected

Draft review:

• Legal review for regulatory compliance
• Technical review for feasibility
• Stakeholder review for practicality

Phase 6: Approve Changes

Follow appropriate approval process.

Minor updates:

• May be approved by policy owner
• Document approval

Major revisions:

• Governance committee review
• Senior management approval
• Board notification (if significant)
• Document full approval chain

Phase 7: Communicate and Implement

Updated policies must reach affected people.

Communication elements:

• What changed (summary of updates)
• Why it changed (rationale)
• What people need to do (action items)
• When it takes effect
• Where to find the updated policy
• Who to contact with questions

Communication channels:

• Email to affected groups
• Intranet announcement
• Team meetings
• Training updates
• Manager briefings

Phase 8: Update Supporting Materials

Don't forget related documents.

Items to update:

• Training materials
• FAQ documents
• Quick reference guides
• Process documentation
• Forms and templates

SOP Outline: Annual AI Policy Review

Purpose: Ensure AI policy remains current and effective through structured annual review.

Timing: [Month] each year

Responsible: AI Governance Lead

Participants: Legal, IT/Security, Risk, HR, Business Representatives

Pre-Review Preparation (Week 1-2)

• Compile trigger events from past 12 months
• Gather accumulated questions and feedback
• Review regulatory developments
• Document new AI tools and uses
• Assess current compliance gaps
• Prepare review briefing document

Review Meeting (Week 3)

Agenda:

1. Review of current policy effectiveness
2. Discussion of trigger events and their implications
3. Review of regulatory developments
4. Stakeholder feedback discussion
5. Identification of needed changes
6. Prioritization of updates

Outputs:

• List of proposed changes
• Assigned drafters
• Timeline for draft completion

Drafting and Consultation (Week 4-6)

• Draft proposed changes
• Legal review
• Stakeholder consultation period
• Incorporate feedback
• Finalize proposed revisions

Approval (Week 7)

• Submit for governance committee review
• Address any concerns
• Obtain required approvals
• Document approval chain

Communication and Implementation (Week 8)

• Update official policy document
• Archive previous version
• Communicate changes to organization
• Update training materials
• Confirm implementation support

Documentation (Ongoing)

• Record review completion
• Document what changed and why
• Update version history
• Set date for next annual review

Implementation Checklist

Process Setup

• Review calendar established
• Triggers defined and documented
• Monitoring responsibility assigned
• Review process documented
• Approval authorities clarified

Each Review Cycle

• Review preparation completed
• Stakeholder input gathered
• Changes drafted and reviewed
• Approvals obtained
• Communication completed
• Supporting materials updated
• Documentation completed

Metrics to Track

• Time since last review: Should not exceed 12 months
• Triggered reviews completed: Number and timeliness
• Policy acknowledgment rates: After updates
• Questions/clarification requests: Trend (should decrease after good updates)
• Gap between policy and practice: Audit findings

Tooling Suggestions

Document management systems: For version control, access, and history. Essential for tracking what changed when.

Policy management platforms: Specialized tools for policy lifecycle management. Good for larger organizations with many policies.

Collaboration platforms: For stakeholder input and review. Comment features enable asynchronous input.

Communication tools: For rollout announcements and training.

Frequently Asked Questions

How often should we review AI policies?

Annual review at minimum. More frequent reviews (semi-annual) may be appropriate during periods of rapid change. Triggered reviews should happen when significant events occur, regardless of calendar.

Who should be involved in reviews?

At minimum: policy owner, legal, IT/security, and business representatives. Broader input leads to better policies. Employee input is valuable but may be gathered via surveys rather than committee participation.

How do we know when a triggered review is needed?

When events occur that the current policy doesn't adequately address. Err toward review when uncertain—better to review and confirm policy is adequate than to miss a gap.

How do we communicate policy changes effectively?

Multiple channels, clear summary of what changed, emphasis on actions required. Don't just email the updated policy—explain the changes and their implications.

Should we track policy exceptions?

Yes. Exceptions often indicate policy gaps or practical issues. Track them and consider whether they should inform policy updates.

How do we handle urgent situations before policy is updated?

Interim guidance can bridge the gap. Document the guidance, its temporary nature, and timeline for formal policy update.

Conclusion

AI policies are living documents. The technology, regulations, and business context they address are changing constantly. Static policies become irrelevant—or worse, create false confidence while actual practices diverge.

Establish a regular review rhythm. Define triggers that initiate out-of-cycle reviews. Follow a consistent process that includes stakeholder input. Communicate changes effectively.

The goal isn't just an updated document—it's policies that actually guide AI use across your organization. That requires continuous attention to keep policies aligned with reality.

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References

• Policy management best practices
• Change management frameworks
• AI governance documentation standards