Incident Response for AI Systems

When an AI system fails in production, the response playbook needs to account for AI-specific failure modes. Traditional incident response focuses on outages - the system is down and needs to be restored. AI incidents are often more nuanced: the system is running but producing harmful, biased, or simply wrong outputs. This requires different detection, escalation, and remediation approaches. Detection is the first challenge. Conventional monitoring catches infrastructure failures, but catching model quality issues requires the performance monitoring discussed earlier. Escalation needs to involve not just engineering teams but also domain experts who can assess whether the model's outputs are appropriate, and potentially legal or communications teams if the outputs could cause harm. Remediation options include rolling back to a previous model version, applying output filters or guardrails, reducing the model's scope (falling back to simpler rules for affected cases), or taking the system offline entirely. The choice depends on the severity and nature of the incident. Post-incident reviews should examine not just what went wrong but what monitoring should have caught it earlier. Many organisations adapt their existing incident response frameworks for AI, adding model-specific runbooks and training their on-call teams to recognise AI-specific symptoms. As AI becomes more embedded in critical business processes, having a clear, practised incident response plan is essential.