AI Incident Response

AI incident response is the prepared operating process for detecting, containing, investigating, and resolving security, compliance, or quality failures in production AI systems. It extends traditional incident response with questions that are specific to models and agents: which model was used, which prompt and context were active, which tools were available, which data sources were reached, and whether the behavior can be reproduced. A strong AI incident process combines runtime telemetry, prompt and tool logs, access-control history, model versions, evaluation results, and a clear escalation path. It defines when to pause an agent, revoke access, switch to a fallback, preserve evidence, inform stakeholders, and restart the system. The evidence discipline matters. If a team overwrites logs, patches blindly, or lets the agent continue acting while the cause is unknown, it can lose the information needed for root-cause analysis and accountability. AI incident response is therefore not just a security playbook. It is an operating capability for organizations that depend on AI systems to take actions, touch sensitive data, or make decisions inside real workflows.