5 AI risk management frameworks for shoring up key gaps
Summary
This article examines five AI-specific risk management frameworks that organizations can use to identify and mitigate AI-related risks, governance gaps, and ethical complexities. The piece discusses how traditional risk management frameworks are insufficient for AI systems and highlights ISO/IEC 42001 as the first internationally recognized formal standard for AI management, alongside other emerging frameworks addressing governance, technical security, threat modeling, and regulatory compliance.
IFF Assessment
The article promotes structured risk management and governance frameworks that help organizations better secure and control AI systems, which benefits defenders and organizations seeking to responsibly deploy AI.
Defender Context
Defenders and security leaders should familiarize themselves with emerging AI risk management frameworks, particularly ISO/IEC 42001, as AI systems introduce novel failure modes and security challenges not addressed by legacy frameworks. Understanding these frameworks is critical for establishing governance structures, data integrity controls, and oversight mechanisms that reduce AI-specific risks such as model poisoning, adversarial attacks, and unintended behavior. Organizations implementing these frameworks will be better positioned to identify where AI deployments can fail and establish appropriate technical and organizational controls.