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Definition of roles, teams, and responsibility assignments for AI governance.
Also in Oversight & Accountability
Risk governance corresponds to the rules and procedures that structure the risk management system in terms of decision-making, responsibilities, authority, and accountability mechanisms
The last part of the risk management framework is risk governance. Risk governance consists of defining the decision-making structure for the risk identification, analysis and evaluation, and treatment components. In essence, it consists of defining "who does what" and "who verifies how it is done," ensuring there are clear roles and responsibilities for decision-making in the risk management processes. Risk governance can be seen as a set of interlocking components that play unique roles and jointly form a cohesive governance structure... these components can be placed in six distinct categories, each fulfilling different purposes. Three categories relate to risk decisions inside the organization, two categories to oversight that is inside the organization, but independent, and one category to the communication of decisions outside the organization. The remainder of this section outlines the six categories, how they relate to each other, and why each one is essential.
Reasoning
Defines roles, responsibilities, and accountability mechanisms for risk decision-making and verification within organization.
Decision-Making
The decisions made by senior management that create or mitigate risk
2.1.1 Leadership OversightAdvisory and Challenge
Risk experts who advise and challenge senior management on their decisions
2.1.2 Roles & AccountabilityCulture
The set of norms, attitudes and behaviors related to awareness, management and controls of risks
2.4.4 Training & AwarenessOversight
Board-level oversight is necessary to provide checks and balances to senior management
2.1.1 Leadership OversightAudit
Independent groups that test the efficacy and sufficiency of the risk management framework and of the risk mitigations
2.2.3 Auditing & ComplianceTransparency
External communication of risks and decision-making
3.1.4 Compliance RequirementsRisk 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.
Other (multiple stages)
Applies across multiple lifecycle stages
Other (multiple actors)
Applies across multiple actor types
Govern
Policies, processes, and accountability structures for AI risk management
Primary
6.5 Governance failure