What does an incident response process look like for LLM applications?

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Same backbone as standard incident management (detect → triage → mitigate → resolve → post-mortem), plus LLM-specific playbooks: quality incidents (hallucination outbreaks, bad outputs), safety incidents (jailbreaks, data leakage, harmful content), provider incidents (outages, silent model changes), cost incidents (runaway loops), and agent incidents (unintended actions). Prioritise fast mitigation levers: kill switches, model or prompt rollback, fallbacks, HITL mode. Preserve traces as evidence.

Incident flow: detect, triage and mitigate fast, then communicate in parallel with investigate, resolve, a blameless post-mortem and prevention.

LLM-specific mitigation levers

Prepare them in advance:

Lever	Use
Feature kill switch	Disable an AI feature, show fallback UX
Auto-action disable	Switch agents to approval-required mode
Prompt / model rollback	Revert to last known good bundle
Provider failover	Route to secondary provider/region
Pattern block	Block a specific jailbreak or abusive input pattern
Rate limiting	Throttle abusive users or runaway clients

Severity examples: SEV1 = data leakage, harmful actions, a major outage; SEV2 = a widespread quality regression; SEV3 = a localised degradation.

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What does an incident response process look like for LLM applications?

LLM-specific mitigation levers

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