How to Build an AI Risk Register

A comprehensive AI risk register is foundational for governance and compliance. This guide shows you how to systematically identify, assess, prioritize, and monitor AI risks across your organization.

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Identify All AI Risks

Begin with comprehensive risk identification across your AI systems. Use structured approaches to ensure you capture all relevant risks:

System Inventory Review: List all AI systems in your organization with their purpose, data inputs, and decision-making scope.
Stakeholder Interviews: Talk with engineers, product managers, compliance, legal, and business teams to understand perceived risks.
Risk Brainstorming: Conduct structured brainstorming sessions using frameworks like STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) adapted for AI.
Historical Analysis: Review incident reports, audit findings, and near-misses to identify recurring risk patterns.
Regulatory Scanning: Review EU AI Act, NIST AI RMF, and industry-specific requirements to identify compliance risks.

Example: Credit Decisioning System
Identified Risks: Algorithmic bias against protected classes, model drift affecting accuracy, data privacy violations, system unavailability, adversarial attacks on input data, lack of explainability for rejected applications.

Assess Risk Impact and Probability

Evaluate each identified risk using standardized scoring. Use a consistent scale for both impact and probability:

Impact Assessment (1-5 scale)

Level 1 (Minimal): No financial impact, no regulatory consequence, minimal user impact
Level 2 (Low): Minor financial impact (<$50K), limited users affected, manageable remediation
Level 3 (Medium): Moderate financial impact ($50K-$1M), significant users affected, regulatory attention possible
Level 4 (High): Major financial impact ($1M-$10M), widespread user impact, regulatory investigation likely
Level 5 (Critical): Catastrophic financial impact (>$10M), systemic failure, regulatory enforcement action

Probability Assessment (1-5 scale)

Level 1 (Remote): Less than 1% chance annually
Level 2 (Low): 1-5% chance annually
Level 3 (Medium): 5-25% chance annually
Level 4 (High): 25-75% chance annually
Level 5 (Very High): More than 75% chance annually

Example Risk Scoring
Risk: "Algorithmic bias in credit decisions" → Impact: 5 (Regulatory enforcement, significant liability) × Probability: 3 (Testing history shows some disparity) = Risk Score: 15 (High)

Define Risk Mitigations

For each significant risk, develop specific mitigation strategies. Mitigation approaches include:

Avoid: Discontinue the risky AI system or use case entirely
Reduce: Implement technical controls (bias testing, anomaly detection, access controls)
Transfer: Use insurance, outsource to managed services, use vendor software with liability terms
Accept: Acknowledge the risk and monitor closely without mitigation (only for low-risk items)

Document for each high-risk item: the specific mitigation action, responsible owner, target completion date, and success metrics. Track mitigation implementation and verify effectiveness through testing and monitoring.

Monitor and Review Continuously

Risk registers are living documents that require ongoing attention. Establish monitoring procedures:

Monthly Review: Check mitigation status, update risk scores based on new information
Quarterly Assessment: Deep dive on high-risk items, identify emerging risks, reassess probability/impact
Annual Audit: Complete re-assessment of all risks, gap analysis against frameworks, certification preparation
Event-Driven Updates: After incidents, near-misses, model retraining, or major system changes

Use your risk register as the foundation for compliance reporting, audit responses, and strategic prioritization of AI governance investments. Share summaries with executive leadership quarterly to maintain governance awareness.

Need Help Building Your Risk Register?

Our risk assessment specialists can guide your team through the identification and prioritization process. We provide templates, scoring frameworks, and remediation roadmaps.

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