The landscape of Responsible AI (RAI) and AI risk management frameworks is rapidly expanding, with a growing number of frameworks that vary widely in scope and approach. This variation has caused confusion and hesitation among organizations eager to implement RAI programs to ensure the safe, ethical design, development, testing, deployment, and governance of AI systems. While organizations recognize the urgency, many are overwhelmed by the complexity and differences between frameworks.

This gap between the accelerating power of AI systems and the pace of responsible governance poses a range of risks, from immediate cybersecurity concerns to broader, large-scale threats. To address this, the Center for Applied AI (C4AI) is building a comprehensive database of publicly available frameworks, risk repositories, incident reports, and related resources. Our goal is to create an accessible, queryable platform that helps organizations navigate the complexity and move forward with confidence.

We will continue to update this page as the project progresses. For more information or to get involved, please contact us at info@c4ai.ai. Below is a small, diverse sampling of the frameworks we are cataloging in our database:

1. NIST AI Risk Management Framework
2. NIST AI RMF Generative AI Profile
3. Microsoft Responsible AI Program and Standard
4. Anthropic Responsible AI Scaling Policy
5. DoD RAI Strategy & Implementation Pathway
6. CDAO RAI Toolkit
7. Army AI Layered Defense Framework
8. CSET AI Harm Framework
9. Responsible AI Institute RAI Framework & Certification
10. The Zero Trust AI Governance Framework