MIT publishes AI risk mitigation database and taxonomy
28/07/2025 | MIT
The MIT AI Risk Repository has developed a draft AI Risk Mitigation Taxonomy, based on an analysis of 13 documents proposing organisational AI risk mitigations. From these, 831 individual mitigations were extracted and categorised to help organisations address AI-related risks.
The taxonomy features four main categories: Governance & Oversight Controls, Technical & Security Controls, Operational Process Controls, and Transparency & Accountability Controls. Operational Process Controls emerged as the most common mitigation category, present in all reviewed documents, with 295 instances. Within the 23 subcategories, Testing & Auditing (127), Risk Management (125), Data Governance (57), Post-Deployment Monitoring (50), and Risk Disclosure (44) were most frequently mentioned. The research aims to provide a structured approach for organisations to implement comprehensive AI risk management strategies.
A related IAPP article about the AI Risk Repository discusses how it will provide a "living inventory of risks, built on the results of a systematic review of 65 documents," including academic papers, policy guidelines and technical assessments, which identified "more than 1,600 distinct risk formulations."
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