AI Incident Response Runbook (triage, communication, remediation)

Purpose and Scope

This runbook helps teams respond rapidly and consistently to incidents involving AI outputs, models, or AI-enabled processes in order to limit harm, restore trustworthy service, meet regulatory expectations, and capture learning. It covers detection, triage, evidence capture, escalation, containment and rollback, communications, remediation, and post-incident review. Use this playbook for production incidents and for serious pre-production incidents that could impact customers, safety, privacy, fairness, or compliance.

Key Hungers Addressed

• Respond rapidly and consistently to AI incidents.
• Preserve useful evidence for investigation and compliance.
• Communicate clearly and appropriately with stakeholders and customers.
• Contain harm, restore safe operation, and prevent recurrence.

Roles & Responsibilities

• Incident Commander – Owns the incident until closed. Coordinates triage, escalation, and communications.
• Technical Lead / ML Engineer – Investigates model, data, and infrastructure; drives technical containment and remediation.
• Forensics / Data Engineer – Captures logs, snapshots, and evidence; ensures chain of custody where required.
• Product Owner – Assesses business impact; prioritizes fixes and user-facing responses.
• Communications Lead – Drafts internal and external messages; coordinates with legal and customer success.
• Legal / Compliance – Advises on regulatory obligations, reporting, and preservation of evidence.
• Customer Success / Support – Executes customer notifications, manages tickets, and records feedback.

Incident Categories (Quick Reference)

• Safety / Harm – Model output causes or could cause physical harm, financial loss, or serious reputational/legal exposure.
• Privacy / Data Leakage – Model exposes sensitive personal or confidential data.
• Bias / Fairness – Outputs show discriminatory or disparate-impact behavior.
• Availability / Functional Failure – Model endpoint errors, latency, or pipeline failures interrupt service.
• Incorrect/Deceptive Output – Hallucinations, wrong facts, or misleading outputs causing user harm.

Triage Checklist (Use immediately)

1. Record time of detection and how it was detected (monitor, user report, audit, test).
2. Assign Incident Commander and notify core response team.
3. Classify incident category and initial severity (Low / Medium / High / Critical).
4. Isolate affected systems or endpoints to prevent further harm (see containment steps).
5. Begin evidence capture (logs, request/response pairs, model version, input data snapshot, configuration, access records).
6. Open an incident tracking record with a unique ID and timeline.
7. Decide and start communications (internal alert, customer notice if required, regulatory notice if required).

Evidence-Gathering Template (capture and preserve)

For every incident, capture and store the following in a tamper-evident manner if required by policy. Keep timestamps and chain-of-custody notes.

• Incident ID and detection timestamp.
• Affected model name, version, model artifact ID, and deployment identifier.
• Runtime environment (container image, server, region, cluster, resource IDs).
• Input(s) that triggered the behavior, request IDs, user/session IDs (sanitized per privacy rules).
• Full model output, including metadata, confidences, and alternative outputs.
• Pre- and post-processing steps, feature values, and any external calls (databases, knowledge bases, external APIs).
• System logs, network traces, and monitoring alerts (time window around the incident).
• Access logs showing who or which systems touched the model or data recently.
• Snapshots of model bias/fairness and evaluation metrics if available.

Containment & Rollback Options (choose safest minimum-impact action first)

• Disable / Quarantine: Temporarily disable the offending model endpoint or take the service offline if immediate harm is likely.
• Switch to Safe Fallback: Route traffic to a human-in-the-loop flow or a validated fallback model with conservative behavior.
• Throttle / Rate-Limit: Reduce request volume to limit exposure while investigation proceeds.
• Feature Flag / Config Revert: Roll back recent configuration changes, prompts, or feature flags affecting model behavior.
• Rollback Model Version: Revert to the previously stable model version if model upgrade caused the incident and rollback is validated.
• Kill External Calls: Disable outbound calls to unreliable external knowledge sources or tools used in the inference path.

Escalation Path (example)

1. Incident Commander (initial handling)
2. Technical Lead (within 15–30 minutes for medium/high incidents)
3. Forensics/Data Engineering (as needed for evidence)
4. Legal & Compliance (if privacy, safety, regulated data involved)
5. Executive Sponsor / CEO (for critical incidents impacting customers, safety, or regulatory standing)

Communication Templates (short, adaptable)

Internal Alert (example)

Subject: [INCIDENT-ID] AI Incident — [short description]
Body: Timestamp, category, services affected, immediate mitigation in place, assigned Incident Commander, next update ETA.

Customer-Facing Notification (example)

We recently detected an issue affecting [service/feature]. We have taken steps to contain the issue and are investigating. We will provide updates by [time window]. If you are impacted, contact [support channel]. We apologize and are working to resolve this safely.

Regulatory / Legal Notice (example)

Keep an evidence package ready: incident summary, impact assessment, mitigation actions, timelines, and contact for follow-up. Consult Legal before sending formal regulatory notices.

Investigation Checklist

• Recreate the incident in a controlled environment if safe and privacy permits.
• Compare model outputs across versions and inputs to narrow root cause (data drift, model change, prompting change, external dependency).
• Review training and recent deployments for data leaks, mislabeled data, or third-party model changes.
• Run targeted tests for bias, safety, or privacy leakage relevant to the incident.
• Log all hypotheses, tests run, results, and decisions in the incident record.

Remediation Plan (brief template)

1. Immediate required actions (containment steps already taken).
2. Short-term fixes (rollback, config change, model retrain with corrected data).
3. Medium-term changes (improve monitoring, add additional tests, retrain and validate).
4. Long-term prevention (pipeline changes, new policies, guardrails, human review workflows).
5. Owner, due date, and verification steps for each item.

Postmortem & Monitoring Updates

After closure, publish a post-incident report that includes timeline, root cause analysis, remediation actions, learned lessons, and an updated monitoring and testing plan. Update runbooks, dashboards, SLOs, and alert thresholds as needed. Ensure verification steps confirm fixes under realistic traffic and adversarial test cases.

Metrics & KPIs to Track

• Mean Time to Detect (MTTD) for AI incidents.
• Mean Time to Contain (MTTC) and Mean Time to Recover (MTTR).
• Number of incidents by category (privacy, fairness, availability, safety).
• False-positive and false-negative rates in monitoring signals used to detect incidents.
• Percent of incidents with preserved evidence sufficient for regulatory/compliance reporting.

Practical Examples & Quick Scenarios

Include short, realisable scenarios such as: hallucination causing false medical advice (safety), model leaking user PII (privacy), a model update increasing toxicity in outputs (fairness/safety), sudden latency/timeout due to external API failure (availability). For each, follow triage → preserve evidence → contain → communicate → remediate → postmortem.

Operationalizing this Playbook

Turn the triage checklist and evidence template into interactive incident forms so responders capture structured information consistently. Use the platform's Interactive Form Rendering and Content Data Submission capabilities to store incident records, generate timelines automatically, and feed dashboards and analytics.

Quick Implementation Suggestions (capability-aware)

• Convert the Triage Checklist and Evidence Template into an interactive Incident Form to capture submissions (use platform InteractiveForm rendering and POST /content/{contentItemId}/submit to save JSON incident records).
• Connect incident submissions to a simple incident dashboard that surfaces open incidents, severity, owners, and MTTR metrics (requires integration beyond this content item).
• Create reusable communication templates in the system and link them to incident records so the Communications Lead can publish consistent messages and record distribution.

Legal, Privacy, and Forensics Notes

Engage Legal early if the incident involves personal data, regulated domains (finance, healthcare, safety-critical systems), or potential civil/criminal exposure. Preserve evidence per legal hold procedures. Sanitize data views when involving external parties.

Next Steps & Continuous Improvement

After each incident, run a blameless postmortem, update the runbook and interactive forms with lessons learned, and add targeted monitoring or tests to prevent recurrence. Treat this playbook as a living tool—teams should adapt it to their environment, technologies, and risk profile.