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Red teaming, capability evaluations, adversarial testing, and performance verification.
Also in Risk & Assurance
Red-teaming activities can be tailored and compared based on the specific deployment circumstances of an AI system. This involves adapting the scope, depth, and focus of red-teaming efforts to match the intended use case, potential risks, and operational context of the AI system. Points of consideration include: • The diversity of potential users and use cases • The sensitivity and impact of the application domain • The scale of deployment and potential reach • Known vulnerabilities or concerns specific to the model or similar systems
Reasoning
Red-teaming activities tailored to deployment context constitute adversarial testing and capability evaluation.
Red teaming
Model development
2.4 Engineering & DevelopmentModel development > Data-related
1.1 ModelModel evaluations
2.2.2 Testing & EvaluationModel evaluations > General evaluations
2.2.2 Testing & EvaluationModel evaluations > Benchmarking
3.2.1 Benchmarks & EvaluationModel evaluations > Red teaming
2.2.2 Testing & EvaluationRisk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems
Gipiškis, Rokas; San Joaquin, Ayrton; Chin, Ze Shen; Regenfuß, Adrian; Gil, Ariel; Holtman, Koen (2024)
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI risk mitigation frameworks is fragmented, uses inconsistent terminology, and has gaps in coverage. This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference. The Taxonomy was developed through a rapid evidence scan of 13 AI risk mitigation frameworks published between 2023-2025, which were extracted into a living database of 831 distinct AI risk mitigations.
Plan and Design
Designing the AI system, defining requirements, and planning development
Deployer
Entity that integrates and deploys the AI system for end users
Measure
Quantifying, testing, and monitoring identified AI risks
Other
