Model Incident Response Runbook

Purpose & Scope

This runbook helps teams respond quickly and consistently to model incidents: unexpected behaviors, performance degradations, bias or fairness concerns, safety issues, or any production behavior that harms customers, business continuity, or regulatory standing. It covers detection, triage, mitigation, communication, investigation, remediation, and post‑incident learning. Use this runbook for all deployed models and automated decision systems.

Key principles

• Act fast to protect customers and systems while preserving evidence for later analysis.
• Contain harm before full diagnosis—temporary mitigations are acceptable if they reduce risk.
• Assign clear ownership early (Incident Lead, Technical Lead, Communications Lead, Legal/Compliance contact).
• Document every action and decision in an auditable record.

Roles & Responsibilities

• Incident Lead: Owns coordination, runbook progress, stakeholder updates, and post-incident review.
• Technical Lead / Model Owner: Runs triage and mitigation steps, performs diagnostics, and leads remediation plan.
• Data/Feature Owner: Validates data, feature pipelines, and recent upstream changes.
• Ops/Platform Engineer: Executes throttles, rollbacks, feature-flag changes, and log exports.
• Legal/Compliance: Advises on customer notifications, reportable incidents, and retention of evidence.
• Communications / Customer Support: Prepares external messaging and coordinates customer outreach when needed.

Triggers — When to Use This Runbook

Start this runbook when one or more of the following occur:

• Monitoring alerts for accuracy, drift, latency, or safety thresholds exceed limits.
• Reports from customers, users, or staff describing harmful or unexpected outputs.
• Detection of model bias or discriminatory outputs in production.
• Significant business metric degradation traced to model behavior.

Severity Levels (Triage)

1. Severity 1 — Critical: Immediate customer harm, legal/regulatory exposure, or total service outage. Requires immediate containment and daily executive briefings.
2. Severity 2 — High: Material customer impact, incorrect decisions affecting business outcomes, or fast-spreading issues. Contain within hours; restore baseline behavior as soon as possible.
3. Severity 3 — Medium: Localized errors, isolated mispredictions, or degraded performance that do not cause immediate harm. Investigate and remediate within normal engineering cycles.
4. Severity 4 — Low / Informational: Anomalies with no observable user impact. Monitor and schedule follow-up analysis.

Initial Triage Checklist (first 60 minutes)

1. Assign Incident Lead and Technical Lead; record start time in incident log.
2. Capture and freeze all relevant artifacts (see Evidence Preservation below).
3. Classify severity using the definitions above.
4. If Severity 1 or 2: notify Legal/Compliance and Communications immediately.
5. Apply immediate mitigations to reduce harm (see Mitigation Actions).
6. Create a single source-of-truth incident record (ticket, doc, or system) and record every action and decision.

Immediate Mitigation Actions (choose least-disruptive first)

Mitigations should be reversible and documented. Test in staging when practical before production changes.

• Throttle model traffic: Reduce request rate or sample fewer requests through the model.
• Failover to baseline: Route decisions to a previously validated rule-based or simpler baseline model.
• Feature pause: Disable suspect features or data pipelines via feature flags.
• Rollback / Revert: Re-deploy previously stable model version if confirmed safe and faster than repair.
• Input validation / blocking: Reject or sanitize inputs that trigger unsafe outputs.
• Human-in-the-loop: Switch to manual review for affected decisions while remaining decisions remain automated if safe.

For each mitigation, record: who executed it, start time, scope, expected impact, and rollback steps.

Communication Templates

Internal Status (short)

Subject: [INCIDENT] Model {model-name} — {Severity} — {short summary}

Body: Start time, Incident Lead, Severity, Summary of observed behavior, Immediate mitigations applied, Next steps, Requested actions and owners.

External Customer Notification (if required)

We are investigating an issue affecting {feature/service}. We have applied temporary mitigations to protect customers and will provide updates at {cadence}. We do not believe customer data has been compromised. Contact {support-contact} for questions.

Legal / Regulatory Alert

Provide: incident start time, model identifier and version, customer impact, mitigations applied, data types involved, and copies of preserved evidence. Request legal guidance for any mandated reporting timelines.

Evidence Preservation (must-do steps)

1. Export model version identifiers, container image tags, and code commit hashes.
2. Snapshot input requests and corresponding model outputs (time-stamped), including metadata (request IDs, user IDs if permitted, feature values, and feature-store versions).
3. Collect monitoring and system logs, alert history, and recent deployments or config changes (with timestamps).
4. Preserve training/validation dataset versions and data provenance records if relevant.
5. Record feature-flag states and pipeline job history around incident time.
6. Store everything in an access-controlled incident evidence repository and log who accessed it.

Root Cause Investigation Template

Use this template during diagnosis. Record findings in the incident record.

• Problem statement: concise description of observed incorrect or harmful behavior.
• Scope & impact: affected customers, transactions, components, duration estimate.
• Hypotheses: list possible causes (data drift, training-serving skew, feature pipeline bug, model regression, upstream service change, adversarial input).
• Evidence & tests: experiments, dataset comparisons, A/B signals, logs, replayed requests, unit/integration checks.
• Confirmed root cause(s): what was determined and why (link to test artifacts).
• Fix plan: code/data changes, validation steps, rollout strategy, monitoring to add/change.
• Estimated timeline and owners for remediation.

Remediation & Validation

1. Develop fix in an isolated branch/environment; include unit tests and dataset checks that capture the issue.
2. Validate fixes using replayed requests, holdout datasets, and safety checks appropriate to the incident type (fairness tests, adversarial tests).
3. Perform staged rollout with close instrumentation and rapid rollback plan.
4. Update monitoring and alert thresholds to detect recurrence and earlier signs.

Post-Incident Review Checklist

1. Confirm incident timeline and final severity classification.
2. Document root cause, corrective actions, and verification evidence.
3. Capture lessons learned and required process changes (e.g., additional tests, guardrails, monitoring).
4. Assign owners for follow-up actions and set deadlines.
5. Share sanitized executive and operational summaries with stakeholders; share technical report with engineering and data teams.
6. Archive evidence and incident artifacts according to retention policy.

Metrics & Reporting

Track: time-to-detection, time-to-mitigation, time-to-recovery, number of affected customers, business impact estimate, and recurrence rate. Use these for process improvements and executive reporting.

Common Pitfalls

• Applying permanent fixes without preserving evidence for later analysis.
• Blaming models without checking upstream data and feature pipelines.
• Delaying communication to stakeholders—transparent, factual updates reduce risk.
• Failing to add tests or monitoring that would detect the same failure earlier.

Templates & Attachments

Attach or link: incident log template, root cause analysis form, stakeholder notification templates, evidence preservation checklist, and post-incident review form.

Appendix: Quick Decision Guide

If customer harm is likely or regulatory exposure possible — escalate to Severity 1 and call Legal immediately. If unsure, err on the side of containment and documentation.