How to Build an AI Risk Register (With the Categories That Actually Matter)

Every AI governance framework — ISO 42001, the EU AI Act, NIST's AI RMF — converges on the same foundational artifact: a risk register for your AI systems. Most companies don't have one. Here's how to build one that survives contact with reality.

Start from the inventory, not the risks

The register's unit of analysis is the AI system, so list those first: models you've built, AI features you've shipped, and — the part everyone forgets — AI embedded in vendor tools your teams adopted without review. For each: what it does, what data feeds it, who owns it, and whose lives or money it touches. A register built on a partial inventory is a false comfort.

Use categories built for AI

Generic operational-risk categories miss what makes AI risky. Assess each system against: accuracy and reliability (hallucination, drift, edge-case failure), bias and fairness (disparate impact on protected groups), privacy (training data, memorization, inference leakage), security (prompt injection, model theft, data poisoning), transparency (can you explain the decision to the person affected?), and dependency (what breaks when the model or its vendor fails?).

Score it like you mean it

Likelihood times impact still works, but define impact in AI terms: regulatory exposure, harm to individuals, reputational blast radius, and operational dependence. Record inherent risk, apply mitigations — human review gates, output filtering, monitoring thresholds, fallback paths — and score residual risk. The delta between the two is your governance program, made visible.

Give every row an owner and a review date

A register nobody rereads is a compliance prop. High-risk systems deserve quarterly review; models get retrained, vendors swap architectures, and a low-risk chatbot quietly becomes a customer-facing decision-maker. The review cadence is what makes the register a governance instrument instead of a snapshot.

Compriska's Enterprise Risk and AI Governance modules do this natively: the AI inventory feeds the register, assessments use AI-specific categories, KRI thresholds trigger reviews, and the executive dashboard shows the residual-risk picture your board actually wants.