AI Incident Response & Forensics Playbook

Purpose and scope

This playbook helps teams quickly detect, triage, and resolve incidents caused by AI outputs, models, or related infrastructure while preserving evidence for forensics, regulatory review, and learning. Use it when an AI system behaves unexpectedly, produces harmful outputs, exposes data, or shows signs of compromise. It focuses on practical steps your incident commander, model owner, SRE/security, and communications teammates can take in the first minutes, hours, and days.

Quick reference: Severity classification

• Severity 1 — Critical: Immediate physical harm, significant regulatory breach, large-scale data exposure, or production outage causing major service disruption. Escalate to executive leadership and legal immediately.
• Severity 2 — High: Widespread erroneous outputs that materially harm customers or business, targeted data leak, or suspected intrusion. Begin full incident response workflows.
• Severity 3 — Moderate: Incorrect or biased outputs affecting a subset of users, degraded model quality, or reproducible errors without immediate harm.
• Severity 4 — Low: Cosmetic issues, single-user complaints, or non-reproducible anomalies with little impact.

Initial triage checklist (first 0–60 minutes)

1. Assign an Incident Commander (IC) and create a dedicated incident channel or bridge.
2. Record the time discovered, reporter, and initial symptoms (inputs, outputs, user actions).
3. Classify severity based on the table above and notify required stakeholders (security, legal, product, SRE, model owner, communications).
4. If immediate safety or data exposure is suspected, enact emergency containment (see containment steps below) before further investigation.
5. Preserve evidence: take immutable snapshots of model endpoints, request logs, system logs, database snapshots, and configuration files. Avoid altering live artifacts when possible.
6. Temporarily limit or disable the AI capability if it is actively causing harm and no mitigations are available (use feature flags, rate limits, or routing to human review).
7. Start an incident timeline and log all actions, decisions, and communications.

Evidence collection & forensics steps

Collect artifacts in a manner that preserves chain of custody and supports later analysis.

• Capture request/response pairs (input prompt, metadata, model output) with timestamps and request IDs.
• Export API gateway, application, OS, container, and cloud provider logs covering the incident window.
• Record model metadata: model name/version/hash, configuration, hyperparameters, weights identifier, training data snapshot identifiers, and deploy manifests.
• Save access logs, authentication events, key rotations, and recent permission changes.
• If applicable, preserve dataset versions and data ingestion logs to detect poisoning or drift.
• Create read-only copies and store them in secured evidence storage with checksums and access records.
• Document every step taken to collect evidence (who, what, when, where, why).

Containment & rollback options

Select the least-disruptive containment that prevents further harm:

• Throttle or quiesce: Apply rate limits or temporarily suspend automated responses for affected endpoints.
• Switch to human-in-the-loop: Route outputs to manual review until resolved.
• Rollback to prior model version: If a recent deployment triggered the issue, consider reverting after evaluating risk.
• Disable features or inputs: Block specific input patterns, remove a dangerous prompt template, or disable model capability flags.
• Revoke keys/access: If compromise is suspected, rotate API keys, revoke compromised accounts, and isolate affected compute instances.
• When rolling back, test in a staging/canary environment before broad redeploy and monitor for recurrence.

Forensics indicators by incident type

Model misbehavior (bias, hallucination, harmful output)

• Indicators: repeated patterns of harmful text, reproducible prompt sequences producing bad outputs, spikes in user complaints or content takedowns.
• Collect: sample inputs/outputs, model version, prompt templates, and training/validation metrics.

Data exposure / privacy incident

• Indicators: outputs that include PII, unexpected file contents, queries returning restricted fields.
• Collect: affected outputs, access logs, dataset lineage, recent data imports, and any downstream usage.

Data poisoning or training integrity

• Indicators: sudden model performance degradation, new adversarial patterns, or targeted degradation against specific cohorts.
• Collect: recent training dataset versions, ingestion pipelines, data provenance records, and validation set results.

Infrastructure compromise

• Indicators: anomalous admin activity, unexpected deployment artifacts, unknown SSH/logins, or container/image tampering.
• Collect: host and container logs, access tokens, CI/CD logs, and recent build artifacts.

Logging, monitoring, and immediate detection signals

Make sure the following are being logged and available to the incident team:

• Per-request inputs/outputs, response confidence scores or safety flags, and request metadata.
• Model performance metrics (accuracy, error rates) and distribution drift statistics.
• Anomaly detection alerts on output content, access patterns, latency, and resource utilization.
• User feedback and abuse reports, mapped to request IDs for traceability.

Communications templates

Internal incident alert (short)

Subject: [IC] AI Incident — [Severity] — [System]

Summary: Briefly state what happened, when discovered, current impact, and immediate containment steps.

Actions: IC assigned — [name]; Model owner — [name]; Security — [name]; Next steps — evidence collection and containment.

Customer advisory (when required)

We identified an issue affecting [feature/system]. We have taken steps to contain the issue and have restored safe operation for most users. We are investigating root cause and will provide updates within [timeframe]. If you believe you were affected, please contact [support channel].

Regulatory/legal notification (trigger template)

Notify Legal and Compliance immediately for suspected data breaches, consumer harm, or jurisdictional reporting obligations. Provide an incident summary, evidence preserved, affected user counts, and mitigation actions taken.

Roles & responsibilities

• Incident Commander (IC): Orchestrates response, makes containment decisions, owns communications cadence.
• Model Owner / Product Lead: Provides model metadata, deploy history, and business context.
• SRE/Platform: Performs containment (feature flags, rollbacks), snapshots, and restores services.
• Security / SOC: Investigates potential compromise and leads forensic collection for infrastructure.
• Legal / Compliance: Advises on reporting obligations and communications.
• Communications: Drafts internal and external messaging.

Post-incident remediation & learning

1. Conduct a root cause analysis (RCA) with evidence-backed findings and a clear remediation plan.
2. Assign owners and deadlines for fixes: model retraining, dataset clean-up, monitoring changes, access control updates, or policy changes.
3. Update runbooks, tests, and deployment gates to prevent recurrence.
4. Communicate lessons learned to stakeholders and run a short blameless post-incident review.
5. Consider customer remediation, regulatory notifications, and any contractual obligations.

Escalation and timeline guidance

• 0–1 hour: assign IC, contain immediate harm, preserve evidence, and classify severity.
• 1–24 hours: full evidence collection, stakeholder notifications, and containment actions (rollback/throttle/human review).
• 24–72 hours: forensic analysis, begin remediation tasks, prepare communications; involve legal if exposure suspected.
• 72+ hours: RCA, remediation completion, and post-incident review.

Practical checks & runbook hygiene

• Maintain a current inventory of deployed models, versions, and owners.
• Run tabletop exercises for AI incidents at least twice per year.
• Keep sample evidence collection commands and scripts in a secure repo for rapid use.
• Document decision gates for rollbacks and when to involve legal or regulators.

When to review this playbook

After any AI-related incident, annually, or when major platform or model changes are made.