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Response plans, escalation procedures, and incident management for operational emergencies.
Also in Operations & Security
Stage: Containment and Mitigation; Stakeholder: Compute Providers; Additional information: Governments and developers should improve safety governance by fostering robust safety cultures and adopting secure-by-design principles. AI developers should evaluate failure modes and implement safeguards before deployment, with independent third-party audits verifying compliance with existing standards, as commonplace in other fields such as nuclear energy, aviation, finance and banking, pharmaceuticals, and more. AI developers should also continue to allocate compute resources to AI safety, including research on monitoring, alignment and safeguards. Governments could make secure-by-design guidelines mandatory to ensure that safety features are built into AI models from the outset (NCSC 2023). They could also consider requirements or incentives for safety research.
Reasoning
Establishes incident response plans and emergency shutdown procedures for operational containment and mitigation.
Monitor critical capability levels
2.2.2 Testing & EvaluationIdentify early warning signs and emergent capabilities
2.2.1 Risk AssessmentEstablish standardised benchmarks and reporting
3.2.1 Benchmarks & EvaluationImplement compute monitoring and anomaly detection
1.2.3 Monitoring & DetectionEnhance hardware and supply chain oversight
2.3.3 Monitoring & LoggingLead efforts to establish shared criteria for AI LOC
3.2.2 Technical StandardsStrengthening Emergency Preparedness and Response for AI Loss of Control Incidents
Somani, Elika; Friedman, Anjay; Wu, Henry; Lu, Marianne; Byrd, Christopher; van Soest, Henri; Zakaria, Sana (2025)
As artificial intelligence (AI) systems become increasingly embedded in essential infrastructure and services, the risks associated with unintended failures rise. Developing comprehensive emergency response protocols could help mitigate these significant risks. This report focuses on understanding and addressing AI loss of control (LOC) scenarios where human oversight fails to adequately constrain an autonomous, general-purpose AI.
Other (multiple stages)
Applies across multiple lifecycle stages
Infrastructure Provider
Entity providing compute, platforms, or tooling for AI systems
Manage
Prioritising, responding to, and mitigating AI risks
