FIELD REPORT · AI
Designing an Actionable AI Risk Register
Structuring impact, likelihood, detectability, and control mapping.
- PUBLISHED
- September 15, 2025
- UPDATED
- May 15, 2026
- READ TIME
- 1 MIN
- AUTHOR
- ONE FREQUENCY
KEY FACTS
- Topic
- ai, governance, risk
- Published
- September 15, 2025
- Last updated
- May 15, 2026
- Read time
- 1 min
- Word count
- 48
An effective AI risk register links each risk to data assets, model families, business processes, and existing mitigations. We score via blended (impact * likelihood * detectability gap) and track residual risk trend over time.
Include lifecycle stage tagging (training, evaluation, deployment, monitoring) to target control investments precisely.
SOURCES
Cited and consulted.
- 01NIST AI Risk Management Framework (AI RMF 1.0)nist.gov
- 02Anthropic Research — Claude model capabilities and safetyanthropic.com
- 03OpenAI Platform Documentationplatform.openai.com
- 04OECD AI Principlesoecd.ai
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