Mapping Frameworks at the Intersection of AI Safety and Traditional Risk Management
Agencies developing AI risk frameworks gain a curated evidence base connecting proven risk management disciplines to AI-specific governance gaps.
Key points
- MIT AI Risk Repository identified 11 frameworks bridging traditional risk management and AI safety, all from 2023 or newer.
- Frameworks draw on proven methods from aviation, nuclear, and cybersecurity to address frontier and general-purpose AI risks.
- APS agencies building AI risk governance could use this as a curated starting point, avoiding duplication of existing work.
Summary
The MIT AI Risk Repository has published an evidence scan identifying 11 frameworks that explicitly bridge traditional risk management and AI safety, covering frontier AI, general-purpose AI, and AGI-level risks. Frameworks span categories including risk management translation (adapting methods from cybersecurity, aviation, and nuclear power) and maturity models for assessing organisational AI risk capability. Primary authors are from the UK, Singapore, Germany, Finland, the USA, and France. The scan is designed to reduce duplication by consolidating existing knowledge and connecting framework creators, and includes links to full texts via a public Paperpile folder.
Implications for Australian agencies
- Consider Agencies developing or refreshing AI risk frameworks could review this evidence scan to benchmark their approach against established international methods and avoid reinventing existing work.
- Monitor Risk and assurance teams may want to monitor the MIT AI Risk Repository for further outputs as this field develops rapidly and new frameworks are expected.
Implications are AI-generated. Starting points, not advice.
"Mapping Frameworks at the Intersection of AI Safety and Traditional Risk Management" Source: MIT AI Risk Repository – Blog Published: 8 April 2025 URL: https://airisk.mit.edu/blog/mapping-frameworks-at-the-intersection-of-ai-safety-and-traditional-risk-management The MIT AI Risk Repository has published an evidence scan identifying 11 frameworks that explicitly bridge traditional risk management and AI safety, covering frontier AI, general-purpose AI, and AGI-level risks. Frameworks span categories including risk management translation (adapting methods from cybersecurity, aviation, and nuclear power) and maturity models for assessing organisational AI risk capability. Primary authors are from the UK, Singapore, Germany, Finland, the USA, and France. The scan is designed to reduce duplication by consolidating existing knowledge and connecting framework creators, and includes links to full texts via a public Paperpile folder. Implications for Australian agencies: - [Consider] Agencies developing or refreshing AI risk frameworks could review this evidence scan to benchmark their approach against established international methods and avoid reinventing existing work. - [Monitor] Risk and assurance teams may want to monitor the MIT AI Risk Repository for further outputs as this field develops rapidly and new frameworks are expected. Retrieved from SIMS, 18 May 2026.