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Independent audits, third-party reviews, and regulatory compliance verification.
Also in Risk & Assurance
Independent groups that test the efficacy and sufficiency of the risk management framework and of the risk mitigations
Without independent groups providing regular checks on risk management processes, they can quickly deteriorate in quality and become purely performative. Audits are provided by internal auditors and/or external auditors. In both cases, they are independent from peer pressure dynamics occurring within the teams dealing with the risk. Internal Audit is a part of the organization, but has a unique reporting line to the Audit committee of the Board to avoid conflicts of interest with the business. It has the mandate to investigate any process in the organization. This is a common function in publicly listed organizations and is a listing requirement of many stock exchanges, such as the New York Stock Exchange (NYSE, 2024). Internal audit teams may lack expertise in certain risk areas, particularly technical risks. Therefore, most industries also use external auditors. These are specialists from outside the organization who are brought in to provide additional assurance expertise. Publicly listed companies in the United States for example must, per the SEC’s (Securities and Exchange Commission) regulations, employ an external auditor for their financial statements (U.S. Securities and Exchange Commission, 2002). External audit providers can also provide assurance in other areas, such as quality or cybersecurity. In AI, external auditors are already used for red-teaming and model capability evaluations.
Reasoning
Independent groups audit risk management framework efficacy through internal and external audits.
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.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Developer
Entity that creates, trains, or modifies the AI system
Govern
Policies, processes, and accountability structures for AI risk management
