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A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management

Simeon Campos, Henry Papadatos, Fabien Roger, Chloé Touzet, Otter Quarks, Malcolm Murray

TL;DR

The paper tackles the lack of systematic risk management for frontier AI by proposing a four-component framework: risk identification, risk analysis and evaluation, risk treatment, and risk governance, all applied across the AI lifecycle. It combines traditional risk-management techniques with AI-specific practices, including classification of known risks, open-ended red-teaming for unknown risks, PRA-like risk modeling, and an explicit risk-tolerance framework expressed through KRIs and KCIs. A key contribution is the three-way relationship linking risk tolerance, KRI thresholds, and KCI thresholds, enabling proactive mitigation and continuous monitoring, with Mitigations categorized into containment, deployment controls, and assurance processes. The framework also details governance structures (decision-making, advisory input, culture, oversight, audit, transparency) and practical artifacts like a risk register, emphasizing pre-training risk work to minimize deployment delays. This approach aims to provide AI developers with actionable, standards-aligned guidelines to manage frontier AI risks more rigorously and transparently, bridging AI practice with established risk-management knowledge to enhance public safety and security.”

Abstract

The recent development of powerful AI systems has highlighted the need for robust risk management frameworks in the AI industry. Although companies have begun to implement safety frameworks, current approaches often lack the systematic rigor found in other high-risk industries. This paper presents a comprehensive risk management framework for the development of frontier AI that bridges this gap by integrating established risk management principles with emerging AI-specific practices. The framework consists of four key components: (1) risk identification (through literature review, open-ended red-teaming, and risk modeling), (2) risk analysis and evaluation using quantitative metrics and clearly defined thresholds, (3) risk treatment through mitigation measures such as containment, deployment controls, and assurance processes, and (4) risk governance establishing clear organizational structures and accountability. Drawing from best practices in mature industries such as aviation or nuclear power, while accounting for AI's unique challenges, this framework provides AI developers with actionable guidelines for implementing robust risk management. The paper details how each component should be implemented throughout the life-cycle of the AI system - from planning through deployment - and emphasizes the importance and feasibility of conducting risk management work prior to the final training run to minimize the burden associated with it.

A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management

TL;DR

The paper tackles the lack of systematic risk management for frontier AI by proposing a four-component framework: risk identification, risk analysis and evaluation, risk treatment, and risk governance, all applied across the AI lifecycle. It combines traditional risk-management techniques with AI-specific practices, including classification of known risks, open-ended red-teaming for unknown risks, PRA-like risk modeling, and an explicit risk-tolerance framework expressed through KRIs and KCIs. A key contribution is the three-way relationship linking risk tolerance, KRI thresholds, and KCI thresholds, enabling proactive mitigation and continuous monitoring, with Mitigations categorized into containment, deployment controls, and assurance processes. The framework also details governance structures (decision-making, advisory input, culture, oversight, audit, transparency) and practical artifacts like a risk register, emphasizing pre-training risk work to minimize deployment delays. This approach aims to provide AI developers with actionable, standards-aligned guidelines to manage frontier AI risks more rigorously and transparently, bridging AI practice with established risk-management knowledge to enhance public safety and security.”

Abstract

The recent development of powerful AI systems has highlighted the need for robust risk management frameworks in the AI industry. Although companies have begun to implement safety frameworks, current approaches often lack the systematic rigor found in other high-risk industries. This paper presents a comprehensive risk management framework for the development of frontier AI that bridges this gap by integrating established risk management principles with emerging AI-specific practices. The framework consists of four key components: (1) risk identification (through literature review, open-ended red-teaming, and risk modeling), (2) risk analysis and evaluation using quantitative metrics and clearly defined thresholds, (3) risk treatment through mitigation measures such as containment, deployment controls, and assurance processes, and (4) risk governance establishing clear organizational structures and accountability. Drawing from best practices in mature industries such as aviation or nuclear power, while accounting for AI's unique challenges, this framework provides AI developers with actionable guidelines for implementing robust risk management. The paper details how each component should be implemented throughout the life-cycle of the AI system - from planning through deployment - and emphasizes the importance and feasibility of conducting risk management work prior to the final training run to minimize the burden associated with it.

Paper Structure

This paper contains 29 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Blind Spots in Existing Safety Policies from AI Companies
  4. Our Approach: Applying Risk Management Techniques to Frontier AI Development
  5. A framework for Frontier AI Risk Management Drawing on Best Practices
  6. Risk Identification
  7. Classification of Applicable Known Risks Using Taxonomies and the Literature
  8. Identification of Unknown Risks Using Open-Ended Red Teaming
  9. Risk Modeling
  10. Risk Analysis and Evaluation
  11. Setting a Risk Tolerance
  12. Operationalizing Risk Tolerance
  13. Risk Treatment
  14. Implementing Mitigation Measures
  15. Continuous Monitoring and Comparing Results with Pre-determined Thresholds
  16. ...and 14 more sections

Figures (2)

  • Figure 1: Key components of the frontier AI risk management framework.
  • Figure 2: Our complete risk management framework along with examples illustrating each component.