Risk Register Template (AI)
Risk Register Template — Artificial Intelligence Systems
ISO 42001 Template Risk Management Toolkit
+ On this page
Key takeaways
- Captures AI risks across design, data, model and post-market stages.
- Each entry is quantified and linked to evidence (EV-IDs) and CAPA logs.
- Supports automated reporting to PMM dashboards and governance reviews.
Purpose & Scope
This template enables teams to record, evaluate and treat AI-related risks in a consistent and traceable manner. It is used during model design reviews, pre-deployment validation, and quarterly AIMS risk assessments.
Register Structure & Field Guide
| Field | Description | Example |
|---|
| Risk ID | Unique identifier for tracking and audit reference. | AI-R-2025-002 |
| Category | Operational / Technical / Ethical / Legal / Reputational. | Ethical |
| Process Stage | Design / Data / Model / Post-Market. | Data Preparation |
| Risk Description | Concise statement of risk scenario. | Biased training dataset causing unequal outcomes. |
| Impact (1–5) | Severity of consequence if realised. | 5 |
| Likelihood (1–5) | Probability of occurrence. | 4 |
| Inherent Risk Score | Impact × Likelihood (before controls). | 20 |
| Controls in Place | Existing technical / organisational measures. | Bias testing script + review board |
| Residual Risk Score | Post-control calculated risk. | 8 |
| Owner | Accountable person or role. | AI Ethics Officer |
| Evidence ID | Link to EV-### artefacts. | EV-FAI-007 |
| Status | Open / Mitigated / Closed. | Mitigated |
| Next Review Date | Scheduled follow-up review. | 2025-12-30 |
Risk Scoring Model & Colour Bands
- Formula: Score = Impact × Likelihood (1 – 25 range).
- Bands: Low (1–5 = Green) / Medium (6–10 = Amber) / High (11–15 = Orange) / Critical (>15 = Red).
- Control Effectiveness Factor: Strong (× 0.5) / Moderate (× 0.75) / Weak (× 1.0).
- Heatmap: Matrix auto-generated in Zoho Analytics or Sheets.
Sample Risk Entries
| Risk ID | Risk Title | Stage | Score | Owner | Status |
|---|
| AI-R-2025-011 | Model drift reducing accuracy below threshold | Post-Market | 12 | ML Ops Lead | Open |
| AI-R-2025-013 | Prompt injection attack on AnswerBot | Deployment | 16 | Security Engineer | Mitigated |
Risk_ID,Category,Process_Stage,Risk_Description,Impact,Likelihood,Inherent_Score,Controls,Residual_Score,Owner,Evidence_ID,Status,Next_Review
Framework Alignment
| Framework | Reference | Relevance |
|---|
| ISO/IEC 42001 | §6.1 & §9.1 | Risk management and evaluation process. |
| NIST AI RMF | Map & Manage | Identification and treatment of AI risks. |
| EU AI Act | Articles 9–10 | Defines mandatory risk management system and controls. |
| UK DSIT Framework | Principle 3 | Encourages systematic and proportionate AI risk oversight. |
Implementation Checklist
- ✅ Template uploaded to central Evidence Repository (EV-RMS-001).
- ✅ Risk Register maintained by AI Risk Officer & reviewed quarterly.
- ✅ Heatmap dashboard active and linked to CAPA Tracker.
- ✅ Cross-reference enabled between risk items and post-market incidents.
- ✅ Audit trail stored for all risk updates with timestamps.
© Zen AI Governance UK Ltd • Regulatory Knowledge • v1 19 Nov 2025 • This page is general guidance, not legal advice.
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