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Staged rollout strategies, phased deployment, and tiered access approaches for production systems.
Also in Operations & Security
AI systems can be released for access incrementally, starting with a small and selected deployer base before progressively being released to a wider user base. Initially, usage to a hosted API can be restricted with access given to specific deployers only, where all instances of the system can be easily updated or decommissioned with minimal disruption should there be any problems identified. Gradual releases provide more time to monitor for vulnerabilities and other problems. Even when such vulnerabilities are detected, the resulting harms may be more limited compared to a scenario in which a more capable version is released with the same vulnerabilities [195].
Reasoning
Staged rollout strategy limits model access incrementally with monitoring controls during deployment.
Model Release
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.
Deploy
Releasing the AI system into a production environment
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
Other
