Mapping AI Risk Mitigations: Evidence Scan and Preliminary AI Risk Mitigation Taxonomy
Citations
0
Open access
No
Source
arxiv
OpenAlex
Not enriched
arXiv
2512.11931
Abstract
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI risk mitigation frameworks is fragmented, uses inconsistent terminology, and has gaps in coverage. This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference. The Taxonomy was developed through a rapid evidence scan of 13 AI risk mitigation frameworks published between 2023-2025, which were extracted into a living database of 831 AI risk mitigations. The mitigations were iteratively clustered & coded to create the Taxonomy. The preliminary AI Risk Mitigation Taxonomy organizes mitigations into four categories and 23 subcategories: (1) Governance & Oversight: Formal organizational structures and policy frameworks that establish human oversight mechanisms and decision protocols; (2) Technical & Security: Technical, physical, and engineering safeguards that secure AI systems and constrain model behaviors; (3) Operational Process: processes and management frameworks governing AI system deployment, usage, monitoring, incident handling, and validation; and (4) Transparency & Accountability: formal disclosure practices and verification mechanisms that communicate AI system information and enable external scrutiny. The rapid evidence scan and taxonomy construction also revealed several cases where terms like 'risk management' and 'red teaming' are used widely but refer to different responsible actors, actions, and mechanisms of action to reduce risk. This Taxonomy and associated mitigation database, while preliminary, offers a starting point for collation and synthesis of AI risk mitigations. It also offers an accessible, structured way for different actors in the AI ecosystem to discuss and coordinate action to reduce risks from AI.
Collections
Add to collection
Paper intelligence
Analysis has not been completed yet.
No graph connections yet.
Sync citations or add papers to shared collections to build this network.
Knowledge graph
Citation network
Explore references, papers that cite this work and related papers in your Codex library.
References
0No references have been linked yet.
Cited by
0No saved paper is currently linked as citing this work.
Related papers
0Add papers to shared collections or enrich their topics to find related work.
Research workspace
Attach the paper PDF, extract its text, classify its contents and create semantic embeddings.
Paper resources
No PDF assets have been attached to this paper yet.