Artificial Intelligence Resilience Maturity Model (AI-RMM) ========================================================== The AI-RMM is a conceptual framework used to assess, measure, and improve the resilience of organisations using or planning to use (AI) Artificial Intelligence. The Model examines how organizations are setup with respect to their AI systems and is structured as a series of levels or stages that represent different degrees of resilience maturity. The Model aligns with the National Institute of Standards and Technology (NIST) AI Risk Management Framework RMF's Core Functions namely, Govern, Map, Measure, and Manage. It is a tool used to evaluate and measure the effectiveness and sophistication of the risk management processes within such a framework. Drawing inspiration from the CERT Resilience Maturity Model (CERT RMM), the Model includes maturity levels (Initial, Managed, Defined, Quantitatively Managed, and Optimizing) that organizations can progress through. These levels reflect the organization's capability to proactively manage and respond to AI-related disruptions, considering factors like governance, workforce diversity, accountability, and engagement with external stakeholders. - [Understanding AI-Aware Organizational Resilience][2] - [AI-RMM Structure and Examples][3] [1]: https://osf.io/x2s7a/files/osfstorage/66a756371c279fa71690fd5a [2]: https://osf.io/kb8a5/wiki/AI-Aware%20Organizational%20Resilience/ [3]: https://osf.io/kb8a5/wiki/AI-RMM%20Structure%20and%20Examples/