Overview & objectives

The AI Policy Suite is the core of Zen AI Governance’s AIMS documentation. It translates strategic AI ethics principles into binding operational rules that govern how AI is developed, deployed, and monitored. The suite ensures alignment with ISO/IEC 42001, EU AI Act (Arts 9–15), UK Data Ethics Framework and ICO guidance.

Policy suite structure

• Master AI Policy – Defines governance structure, roles, risk principles, and compliance expectations.
• Transparency Policy – Explains user disclosure, explainability, and rights to human review.
• Model Lifecycle Policy – Controls development stages (Design → Deploy → Monitor → Retire).
• Incident & CAPA Policy – Describes incident logging and corrective/preventive actions.
• Supplier Governance Policy – Ensures third-party AI services meet standards.
• Data & Privacy Policy – Specifies lawful basis, data minimisation, and retention.

Master AI Policy

• Sets overall AI governance objectives and risk appetite approved by the Authorising Officer.
• Outlines organisational structure (AI Governance Board, Ethics Committee, Oversight roles).
• Includes principles of fairness, explainability, accountability and robustness aligned with EU/UK AI ethics.
• Establishes annual review cycle with audit evidence and change control tracking.

Transparency & disclosure policy

• Defines when and how users must be informed they are interacting with AI (Art 52 AI Act).
• Specifies channels for publishing system cards, bias reports and explainability statements.
• Provides templates for “AI in use” banners, help articles, and human-review requests.
• Requires annual review to ensure accuracy of disclosures and compliance with privacy laws.

AI lifecycle controls

The Lifecycle Policy links technical governance and compliance at each stage of the AI system:

1. Design & Planning – Perform risk assessment and ethical impact review before development.
2. Development – Bias testing, data provenance, traceability, and security validation.
3. Pre-Deployment – Technical validation, compliance sign-off, and human oversight readiness check.
4. Deployment – Controlled release via AI-CAB approval and change management.
5. Monitoring & Improvement – PMM metrics, incident tracking, and drift analysis.
6. Decommissioning – Data archival, model retirement records, and evidence closure.

Records & document control

• Policies stored in version-controlled repository (WorkDrive or SharePoint).
• Each policy assigned owner, review date and approval signature.
• Archived versions retained ≥ 5 years for audit traceability.
• Controlled distribution — only current versions visible to staff in portal.

Links to AIMS & risk

• Each policy mapped to ISO 42001 clauses and risk register entries.
• Policy changes trigger risk review and CAPA updates within AIMS.
• Policy review outcomes reported in Management Review and AI Governance Board minutes.

Templates & examples

Policy: AI Transparency Policy v1.2    Owner: Compliance Lead    Approved: AO 09-Nov-2025  
Scope: All customer-facing AI systems with decision impact.  
Linked Risks: R-AI-004 (Bias), R-AI-007 (Explainability).  
Next Review: 09-Nov-2026    Storage: /WorkDrive/AIMS/Policies/TransparencyPolicy_v1.2.pdf
  

Common pitfalls & mitigation

• No policy ownership: assign clear owners and approval hierarchy.
• Out-of-date versions: maintain central repository and automated reminders.
• Fragmented controls: integrate policy review into management review agenda.
• No traceability: cross-link policies with risk register and CAPA tracker.

Implementation checklist

• Policy Suite documented and approved by Authorising Officer.
• Version control & record index in place.
• Lifecycle Policy linked to change management workflow.
• Transparency disclosures published and auditable.
• Annual policy review evidence stored for audit.

© Zen AI Governance UK Ltd • Regulatory Knowledge • v1 09 Nov 2025 • This page is general guidance, not legal advice.