Safety Frameworks and Standards: A comparative analysis to advance risk management of frontier AI

This document systematically compares Frontier Safety Frameworks (FSFs) with international risk management standards, identifying areas of convergence, divergence, and opportunities for mutual reinforcement. FSFs have emerged rapidly in response to pressing governance needs, introducing concrete mechanisms such as capability thresholds, predefined frontier risks, evaluation triggers, and, in some cases, incident reporting channels and external assessments. While FSFs are agile and provide pragmatic, frontier-specific features, they often leave implicit fundamental considerations, including the definition of risk, the rationale for threshold selection, and the mapping of evaluation outcomes to risk severity and likelihood, to cite a few. By contrast, international risk management standards offer a mature vocabulary and structure, refined through consensus and validated through decades of application in high-stakes sectors. They emphasize assessing the adequacy of the overall risk management system, the justification of risk criteria, systematic and comprehensive processes, including for risk identification and analysis, clarity in linking assessment results to risk, and evaluation of the effectiveness of specific techniques, among others. However, these standards frequently remain abstract, were not designed with frontier AI in mind, and are not updated at a pace commensurate with its rapid development. The analysis concludes that integrating the systematic rigor of international standards with the frontier-specific innovations of FSFs offers a promising path toward more coherent, effective, and internationally harmonized practices. The document provides recommendations for the development of future standards for frontier AI risk management.

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