Overview & purpose

This framework defines how Zen AI Governance UK Ltd develops and sustains competence in AI governance, ethics, risk management and compliance. It ensures every person involved in AI design, deployment or oversight has the skills to act responsibly and recognise risk signals in line with EU/UK requirements and ISO/IEC 42001.

Framework structure

• Policy layer: sets obligations for training and competence maintenance.
• Role matrix: defines competence levels (Awareness, Working, Expert).
• Curriculum design: modular learning aligned to risk roles & use cases.
• Verification & audit: training records stored and sampled during surveillance audits.

Competence by role

Training curriculum

• Core modules: AI ethics, ISO 42001 principles, NIST AI RMF overview, EU AI Act obligations.
• Role-specific modules: oversight design, bias testing, PMM logging, data protection impact assessments.
• Scenario-based learning: simulated AI incidents and CAPA resolution.
• Micro-learning: monthly 5-minute refreshers on AI ethics cases and updates.

Delivery & frequency

• Initial training at onboarding for all AI-involved roles.
• Annual refresher or upon major framework updates.
• Advanced training for new technologies (e.g., LLMs, RAG systems, agentic AI).
• Training records retained ≥ 3 years; attendance ≥ 90 % mandatory for audit compliance.

Records & verification

• Maintain digital training register with names, dates, modules, scores, and certificates.
• Attach evidence (PDFs, screenshots, LMS reports) to AIMS document control system.
• Auditors sample records annually for random verification of competence.

Assessment & certification

• Knowledge checks post-training (min 80 % pass mark).
• “Competent” status granted only after assessment + supervised practice.
• Maintain competence matrix reviewed quarterly by AIMS Manager.

Governance & ownership

• AIMS Manager: owns policy and training plan.
• Department Heads: track attendance and completion in LMS.
• Compliance Lead: verifies records and approves training vendors.

Awareness & culture

• Monthly AI governance newsletter with incident lessons and policy updates.
• Internal “AI Ethics Week” to reinforce culture and share good practice.
• Gamified learning badges for staff who complete advanced modules.

Templates & examples

Name: Priya Malik  |  Role: Oversight Officer  |  Date: 2025-10-18  
Modules: AI Ethics 101 (90 %), Bias Testing (85 %), ISO 42001 Core (94 %)  
Competence Level: Expert  
Assessment: Passed | Trainer: Dr Lewis Graham | Verification: AIMS Manager 2025-10-20
  

Common pitfalls & mitigation

• One-off training: build continuous learning loops with refreshers and updates.
• No evidence: store attendance proof and scores centrally in AIMS.
• Generic content: tailor modules to real AI use cases and roles.
• Skill decay: use competence reviews every 12 months with gap analysis.

Implementation checklist

• Training & Competence Policy approved and published.
• Competence Matrix defined per role and linked to risk register.
• All AI-involved staff trained and assessed annually.
• LMS or register maintains verifiable records.
• Quarterly competence review and gap closure actions logged.

© Zen AI Governance UK Ltd • Regulatory Knowledge • v1 08 Nov 2025 • This page is general guidance, not legal advice.