AI systems fail differently to traditional software. An outage is still an outage, but you also get “soft failures”: plausible answers grounded in nothing, unsafe completions that slip past filters, and tool calls that take unintended actions. Without a clear playbook, teams respond slowly and make changes that accidentally widen the blast radius.
An AI incident response model should be familiar—detect, triage, contain, remediate, prevent—but the mechanics need to reflect prompts, retrieval, models and tools as first-class moving parts.
Define what an AI incident is
Start by agreeing on incident types and severity. Common categories include:
- Quality regressions. Hallucinations, wrong answers, or retrieval failures that materially impact decisions.
- Safety and policy breaches. Disallowed content, unsafe advice, or broken refusal behaviour.
- Data exposure. Prompt or retrieval paths leaking sensitive data across users, tenants or roles.
- Tool failures. Incorrect tool arguments, duplicated actions, or automation that executes the wrong intent.
- Cost and latency incidents. Token explosions, timeouts, and cascading retries.
Tie severity to impact (customer harm, compliance, financial exposure) rather than model-centric metrics. Use an explicit “stop the line” rule for high-risk workflows with tool access.
Detect early with leading signals
Traditional SLOs still apply, but you also want AI-specific indicators:
- Refusal and escalation rates. Spikes can indicate broken policies or degraded retrieval.
- Tool-call validity. Parse errors, schema mismatches, and “tool loop” patterns.
- Groundedness drift. Falling citation coverage or lower answer-evidence alignment.
- Prompt injection signals. Repeated attempts to override system instructions or exfiltrate data.
Instrument these through a combination of observability (AI observability), guardrails (guardrails) and SLO definitions (AI SLO playbook).
Containment levers you can pull safely
Good incident response relies on fast, reversible levers. Build them before you need them:
- Kill switches. Disable tools, disable retrieval, or force human escalation for specific intents.
- Safe fallbacks. Route to a “safer” model or a narrower prompt for high-risk flows (see routing and failover).
- Context throttles. Reduce token budgets, drop optional memory, and cap tool output length.
- Knowledge base freezes. Pause ingestion if new content is suspected to be poisoning retrieval results.
Containment should never require a redeploy. If it does, it will be used too late.
Triage requires replayability
When an incident occurs, you need to reproduce the exact conditions: prompt version, model version, retrieval IDs, tool schemas, tool outputs and policy settings. Without that, teams argue about anecdotes instead of isolating causes.
Maintain an evaluation sandbox and harness that can replay production traces into a safe environment. This also helps you turn incidents into test cases, which is the main way AI quality improves over time.
Post-incident: turn lessons into guardrails
Effective postmortems create durable changes:
- Add regression tests to your evaluation suite for the specific failure mode.
- Update prompts, retrieval filters, or tool schemas with versioned change control.
- Run targeted adversarial testing (see LLM red teaming) for similar attack surfaces.
- Clarify ownership: who owns the knowledge base, tool actions, and policy approvals.
AI incident response is ultimately a governance capability. Teams that build it early move faster later—because they can safely take risks and recover quickly when something breaks.