Mapping AI Risk Mitigations

Summary

MIT's "Mapping AI Risk Mitigations" represents the most comprehensive living database of AI risk frameworks available today. This systematic review goes beyond simple cataloging—it actively maps the relationships between different risk mitigation strategies across the AI ecosystem. What sets this repository apart is its focus on multi-agent risks and its dynamic taxonomy that evolves with emerging threats. Rather than presenting isolated frameworks, it creates a unified lens through which practitioners can understand how different risk assessment approaches complement, overlap, or conflict with each other.

The Innovation Behind This Repository

This isn't just another collection of AI risk papers. MIT has created a living system that treats risk frameworks as interconnected components of a larger governance ecosystem. The repository introduces domain taxonomy specifically designed for multi-agent risks—addressing scenarios where multiple AI systems interact in ways that create emergent risks not present in single-system deployments. This forward-thinking approach recognizes that tomorrow's AI risks will likely emerge from system interactions rather than isolated AI behaviors.

What You'll Discover

The repository structures AI risk knowledge across several dimensions:

• Framework Relationships: See how NIST's AI RMF connects to ISO/IEC 23053, where the EU AI Act's risk categories align with academic research, and which industry frameworks fill gaps in regulatory guidance.
• Multi-Agent Risk Mapping: Explore risk scenarios unique to environments with multiple AI systems, including competitive dynamics, coordination failures, and systemic risks that emerge from AI-to-AI interactions.
• Mitigation Strategy Cross-Reference: Understand which mitigation approaches work across different risk types and which are specific to particular domains or deployment contexts.
• Evolving Threat Landscape: Access regularly updated analysis that incorporates new research, regulatory developments, and real-world incidents into the existing framework mapping.

Who This Resource Is For

• AI Risk Managers and Chief AI Officers will find this invaluable for developing comprehensive risk strategies that don't rely on a single framework. The cross-framework mapping helps identify blind spots in current approaches.
• Policy Researchers and Regulatory Affairs Teams can use this to understand how emerging regulations fit within the broader risk management landscape and identify areas where policy guidance may be lacking.
• AI Safety Researchers will appreciate the systematic approach to cataloging mitigation strategies and the identification of research gaps, particularly in multi-agent scenarios.
• Enterprise AI Teams implementing risk management programs can use this to select complementary frameworks rather than betting everything on a single approach.
• Standards Organizations can leverage this mapping to identify overlaps, gaps, and opportunities for harmonization across different standardization efforts.

How to Navigate This Living System

Start with the domain taxonomy to understand how MIT categorizes different types of AI risks. This provides the conceptual foundation for everything else in the repository.

Use the framework comparison matrices to identify which existing frameworks best address your specific risk concerns. The repository doesn't just list frameworks—it analyzes their coverage, strengths, and limitations.

Pay special attention to the multi-agent risk sections if you're dealing with AI systems that will interact with other AI systems, compete in markets, or operate in environments with multiple autonomous agents.

Bookmark the repository and return regularly—as a living resource, it incorporates new frameworks, updates existing analysis, and expands coverage based on emerging risks and mitigation strategies.

The Multi-Agent Advantage

Traditional AI risk frameworks often assume single-system deployments. MIT's repository explicitly addresses the growing reality of multi-agent environments where risks emerge from interactions between AI systems. This includes competitive dynamics between AI agents, coordination problems in multi-agent systems, and systemic risks that only appear at scale. This focus makes the repository particularly valuable for organizations deploying AI in complex, multi-stakeholder environments.