Originally published at https://charz.ai/blog/ai-risk-register-build-maintain by Char-Z AI.
What an AI Risk Register Is
An AI risk register is a structured, living record of the risks associated with your AI systems — what could go wrong, how likely and severe it is, and what you are doing about it. It is the operational heart of AI risk management, and it is the natural home for the output of the NIST AI RMF's Map, Measure, and Manage functions (NIST, 2023).
A register that works is used in decisions, not just maintained. It tells leadership where to focus, auditors what was considered, and teams what to fix first. The value comes less from the document and more from the disciplined review loop around it.
Core Fields per Risk Entry
- ID — stable identifier (e.g. RISK-001).
- System — the AI system the risk applies to.
- Description — what could go wrong and why.
- Category — e.g. data, model, vendor, usage, regulatory.
- Likelihood — low / medium / high.
- Impact — low / medium / high.
- Score — combined likelihood × impact.
- Mitigation — what you’re doing (or plan to do) about it.
- Owner — who is accountable.
- Status — open / treating / monitored / accepted / closed.
- Due date — when treatment is expected.
- Last reviewed — when the entry was last validated.
How to Build One
Step 1: Seed from your systems.
Starting from your AI system inventory, brainstorm risks per system. Generate a first-pass list of likely risks — data exposure, harmful output, bias, vendor failure, regulatory change, misuse.
Step 2: Score each risk.
Assign likelihood and impact using a consistent rubric so scores are comparable across systems (see our NIST gap-assessment guide for a simple three-level rubric).
Step 3: Assign treatment and owner.
For each risk above appetite, record a mitigation, its owner, and a due date. Risks you choose not to treat are documented as accepted.
Step 4: Prioritize and review.
Sort by score, review the register with the governance working group on a regular cadence, and update the status of each open risk.
Keeping It Alive
Staleness is the register's biggest enemy. Keep it current with:
Monthly treatment review — update the status of open risks and their mitigations.
Re-scoring on change — re-assess when a system's purpose, data, or vendor changes (data-flow mapping and system inventory feeds this).
Incident loop — every AI incident generates a risk entry or updates an existing one (see our incident response playbook).
Annual full review — re-score everything and close or re-open entries.
Roles
- Risk owner — provides input per system.
- Governance lead — maintains the register + cadence.
- Working group — reviews and decides treatment.
- Executive sponsor — escalation + appetite decisions.
A Practical Note on Tools
A spreadsheet is a fine starting point for a small team — the register's value is the loop, not the tool. As you scale, a structured tool with fields, owners, and automation beats a document. Whatever you use, the discipline that matters is the review cadence, not the container.
Sources
NIST. (2023). *Artificial Intelligence Risk Management Framework (AI RMF 1.0)*. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
Top comments (0)