AI Risk Register

Purpose

An AI Risk Register extends your existing risk management process — identify, assess, mitigate, monitor — to cover the risk categories that AI introduces and standard IT registers don't address: bias and fairness, transparency, accountability, adversarial robustness, and societal impact. The mechanics stay the same. The taxonomy changes.

Unlike a traditional project risk register, an AI risk register does not close at deployment. EU AI Act Article 9 defines risk management as a continuous iterative process throughout the AI system's lifecycle. The register stays active for as long as the system operates — updated when the model is retrained, when data sources change, when scale increases, or when monitoring surfaces new patterns.

Where It Fits in Your Document Pack

Read the full article: AI Risk Registers: Managing Risks That Didn't Exist Last Year →

The Risk Register draws its initial risk inventory from the AI Impact Assessment — the seven impact domains map directly to the seven AI risk categories. It then feeds into every downstream governance document: your Human Oversight Plan references high-residual risks, your Post-Deployment Monitoring Plan tracks the detection methods logged here, and your incident log traces findings back to register entries.

What This Template Covers

• Register identification: ID, version, status, owner, and review cadence
• System context: deployment environment, EU AI Act risk classification, and linked documents
• Risk category coverage: all 7 AI risk categories with in-scope / N/A flags and scoping rationale
• Assessment methodology: scoring method and linked governance documents
• Risk entry block: 14-field structure per risk — ID, category, severity, description, affected parties, owner, likelihood, impact, risk score, detection method, response strategy, mitigation actions, residual risk, and monitoring approach. Duplicate the page for additional entries.
• Review trigger field: events that prompt reassessment of each entry
• Post-deployment monitoring plan: review cadence, metrics, post-deployment owners, and incident log location
• Review and sign-off with three-tier approval: register owner, PM/sponsor, governance
• Revision history with trigger column — tracks what prompted each update
• Completion guidance page with field-by-field instructions including the AI-specific likelihood model

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