Research Summary: Bridging AI Risk Frameworks: Reconciling ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act into a Uni ed Governance Taxonomy

Analysis of current AI governance highlights the structural heterogeneity and potential inconsistencies among three primary instruments: ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act. Despite a shared objective of trustworthy AI, these frameworks diverge significantly in legal status, governance scope, and risk interpretation, leading to incomplete or misleading practical applications of their crosswalks.

Why it matters

The divergence in leading AI governance frameworks creates a complex regulatory and operational landscape for organizations developing and deploying AI. Understanding these differences and their implications is crucial for ensuring compliance, managing risk effectively, and maintaining trust in AI systems across varied jurisdictions and operational contexts.

Key insights

• Three distinct AI governance instruments (ISO/IEC 42001, NIST AI RMF 1.0, EU AI Act) are emerging as central to the field.
• These instruments differ fundamentally in their legal status (certifiable standard, voluntary framework, binding law).
• They also vary in their conceptualization of risk and the specific aspects of governance they address.
• Current practical attempts to reconcile these frameworks through 'control-level crosswalks' are often incomplete and can be misleading.
• The overarching goal of all three instruments is to ensure trustworthy AI.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.07515