MIT Launches AI Risk Repository: A Comprehensive Resource for Risk Mitigation

Another important milestone from the MIT AI Risk Repository Initiative: the release of the AI Risk Repository and the draft AI Risk Mitigation Taxonomy.

This work is designed to make life easier for organizations, consultants, and auditors who are grappling with AI risks. Instead of reinventing the wheel, they can now draw on 831 extracted controls from 13 key frameworks (2023–2025), systematized into a structured repository.

The draft taxonomy organizes AI risk mitigations into four major categories:

1️⃣ Governance & Oversight Controls → Board oversight, risk management, whistleblower protections, safety decision frameworks, societal impact assessments.

2️⃣ Technical & Security Controls → Model & infrastructure security, alignment techniques, safety engineering, content safety.

3️⃣ Operational Process Controls → Testing & auditing, hasztag#datagovernance, access management, staged deployment, post-deployment monitoring, incident response.

4️⃣ Transparency & Accountability Controls → Documentation, risk disclosure, incident reporting, governance disclosure, third-party access, user rights & recourse.

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Key Findings

✅ 831 mitigations catalogued, spanning technical, organizational, and policy domains.

✅ 295 focused on operational processes, proving that ongoing monitoring and incident response are central to AI risk management.

✅ Operational Process Controls were the most common (36% of all mitigations), with Testing & Auditing and Risk Management leading the way.

✅ Risk management is still an emerging concept: definitions differ across documents, and organizations risk treating “any action” as risk management.

✅ Testing & auditing (e.g., red teaming, audits, bug bounties) is the most frequently cited subcategory, reflecting demand for accountability and verification.

✅ Some critical areas — like model alignment and staged deployment — remain underrepresented and need more attention.

This is another great work from the MIT team. Organizations, consultants, and auditors can now integrate these extracted controls into their environments without the burden of creating them from scratch. Security for agentic and advanced AI is still new, and professionals are learning how to secure architectures, data flows, and testing approaches. A repository like this bridges the gap and provides a ready-to-use foundation for AI governance, compliance, risk, privacy, and cybersecurity."

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Author: Sebastian Burgemejster