More on AI Frameworks: Expanding your toolkit

ISO 42001 is the first international standard specifically for Artificial Intelligence Management Systems. Where NIST AI RMF focuses on risk management, ISO 42001 takes a broader management system approach, similar to how ISO 27001 addresses information security management. It covers AI governance, risk management, and controls across the AI lifecycle, with a strong emphasis on ethics, transparency, fairness, accountability, and trustworthiness. For organizations already operating within the ISO framework family, ISO 42001 is a natural fit.

The Cloud Security Alliance AI Controls Matrix, or AICM, is a vendor-neutral framework designed specifically for cloud-based AI systems. It includes 243 control objectives across 18 security domains and aligns with NIST AI RMF, ISO 42001, and the EU AI Act. For organizations whose AI footprint is primarily cloud-based, which describes most modern enterprises, the AICM offers highly specific, actionable guidance on model security, adversarial attack mitigation, data poisoning prevention, and identity and access management for AI systems. It's free to download.

The FAIR-AIR framework applies the Factor Analysis of Information Risk methodology to AI risk. Where most frameworks are qualitative, FAIR-AIR is explicitly quantitative: it helps organizations assign dollar values to AI loss exposure scenarios, enabling risk-based decision-making with the same rigor you'd apply to cyber risk. The output is statements like "There is a 5% probability in the next year that employees will leak sensitive information via an open-source LLM, leading to $5 million in losses." That kind of precision is invaluable for communicating AI risk to boards and executives.

MITRE's ATLAS Matrix (Adversarial Threat Landscape for Artificial Intelligence Systems) is the framework of choice for organizations focused specifically on adversarial AI risks. It documents attacker tactics and techniques targeting AI systems across multiple industry domains, and provides guidance on countermeasures. If your threat model includes sophisticated adversaries targeting your AI systems, ATLAS belongs in your toolkit.

Google's Secure AI Framework provides guidance for integrating security into the AI development lifecycle, with a strong emphasis on threat modeling and continuous monitoring. HITRUST AI extends the established HITRUST Common Security Framework to address AI-specific requirements, making it particularly relevant for healthcare organizations already operating within HITRUST.

The right answer for most organizations isn't to pick one framework and ignore the rest. It's to select a primary framework that aligns with your existing risk management infrastructure, and then supplement it with domain-specific guidance from others as needed. The goal is comprehensive coverage of AI risk, not framework purity.