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Runtime monitoring, observability, performance tracking, and anomaly detection in production.
Also in Operations & Security
Ongoing monitoring of AI systems can uncover emergent or newly identified capabilities and limitations, in particular when new use cases are found, or in a large-scale deployment with a diverse population of users. These new capabilities or limitations can inform ongoing risk analysis. New use-cases can be discovered via monitoring publications, online forums, or APIs [131]. For example, a LLM might have unexpectedly high competence at giving convincing medical advice, despite not being directly developed for that purpose, nor verified for accuracy. In that case, the potential risks of this newly found competence can be assessed
Reasoning
Runtime monitoring tracks deployed system for unexpected use patterns and behavioral anomalies post-deployment.
Monitoring
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.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Deployer
Entity that integrates and deploys the AI system for end users
Measure
Quantifying, testing, and monitoring identified AI risks
Other
