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Structured analysis to identify, characterize, and prioritize potential harms and risks.
Also in Risk & Assurance
Risk modeling is the systematic process of analyzing how identified risks could materialize into concrete harms. This involves creating detailed step-by-step scenarios of risk pathways that can be used to estimate probabilities and inform mitigation strategies.
Based on risks identified in the literature and during open-ended red-teaming exercises, AI developers should create detailed scenarios mapping how an AI model’s capabilities or propensities could lead to real-world harms. These scenarios should break down complex risk pathways into discrete, measurable steps... The results of the risk modeling work should be well documented, including the methodologies used, the experts involved, and the list of identified scenarios. This documentation should be shared with relevant stakeholders and used to develop effective evaluation strategies, as well as effective risk mitigation strategies
Reasoning
Systematic analysis identifying and characterizing potential AI harms through detailed scenario modeling and risk pathway analysis.
Risk Analysis and Evaluation
Risk analysis and evaluation is a process that starts with the definition of a risk tolerance. This risk tolerance is then operationalized into risk indicators and their corresponding mitigations required to reduce risk below the risk tolerance.
2.2.1 Risk AssessmentRisk Analysis and Evaluation > Setting a Risk Tolerance
A risk tolerance represents the aggregate level of risk that society is willing to accept from AI systems.
3 EcosystemRisk Analysis and Evaluation > Operationalizing Risk Tolerance
Risk tolerance must be operationalized into measurable criteria to be practically useful in day-to-day operations. A risk tolerance can be translated into (1) Key Risk Indicator (KRI) thresholds, which are thresholds on measurable signals that serve as proxies for risks, and (2) Key Control Indicator (KCI) thresholds, which are thresholds on measurable signals that serve as proxies for the level of mitigation achieved.
2.2.1 Risk AssessmentRisk Treatment
Risk treatment corresponds to the process of determining, implementing, and evaluating appropriate risk-reducing countermeasures
2.2 Risk & AssuranceRisk Treatment > Implementing Mitigation Measures
AI developers should operationalize their KCI thresholds into mitigation measures.
2.3 Operations & SecurityRisk Treatment > Continuous Monitoring and Comparing Results with Pre-determined Thresholds
Developers must therefore implement continuous monitoring of both KRIs and KCIs to ensure that KCI thresholds are met once KRI thresholds are crossed according to the predefined "if-then" statements established in the risk analysis and evaluation phase.
2.3.3 Monitoring & LoggingA Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management
Campos, Simeon; Papadatos, Henry; Roger, Fabien; Touzet, Chloé; Quarks, Otter; Murray, Malcolm (2025)
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.
Plan and Design
Designing the AI system, defining requirements, and planning development
Developer
Entity that creates, trains, or modifies the AI system
Map
Identifying and documenting AI risks, contexts, and impacts
Primary
7 AI System Safety, Failures & Limitations