AI Incident Response Playbook

# AI Incident Response Playbook

Sample Deliverable

Executive Summary

This playbook defines how to respond when an AI system leaks restricted data, follows malicious retrieved instructions, takes or queues unsafe tool actions, violates provider boundaries, or mishandles AI traces.

AI incidents require normal incident discipline plus AI-specific evidence. The team must preserve prompt envelopes, retrieved chunks, source ACL metadata, model routes, outputs, policy decisions, approval records, tool calls, and trace references before they disappear.

Heads up

Public sample notice

This is a shortened, synthetic excerpt prepared as a public sample. A client version would include system-specific evidence, implementation references, architecture screenshots, control test results, owner sign-offs, and full supporting documentation. This sample uses Northstar Support Cloud / Customer Support Copilot as the synthetic reference system. This sample is not legal advice, not a compliance certification, not an audit opinion, not a warranty, and not proof that any unreviewed system is secure.

Decision · planned

Incident readiness decision

Create and tabletop the AI incident response playbook before making strong trust-center claims about AI incident readiness.

Metrics

Incident Playbook Snapshot

Incident classes

Severity levels

Response phases

Customer notification triggers

Tabletop scenarios

Note

AI incident response starts with trace preservation

If the team cannot reconstruct the prompt, retrieval, model route, tool call, approval, and output path, it cannot confidently explain or contain the incident.

Incident classes

Evidence pack

AI Incident Class Map

The playbook maps incident classes to severity defaults, owners, evidence to preserve, and first containment actions.

Synthetic public-safe AI incident response playbook covering incident triggers, severity, triage, trace preservation, containment, reconstruction, communication, customer notification, and recovery.

implemented

partial

missing

planned

AI incident classes

Incident class	Default severity	Primary owners	First containment
Restricted data appears in AI answer	Critical	Security Operations, Search Platform, Product Security	disable source or index route
Prompt injection changes model behavior	High	Product Security, AI Platform Engineering	quarantine poisoned source or prompt route
Unsafe AI-assisted tool action	Critical	Security Operations, AI Platform, Product Operations	disable tool route or credential
Model provider boundary issue	High	Vendor Management, Legal, Security Operations	disable affected provider route
AI trace exposure or retention failure	High	Security Operations, Security Engineering, Privacy	restrict trace access and preserve audit logs

Severity rubric

AI incident severity rubric

Severity	Criteria	Executive notification
Critical	restricted data exposure, unauthorized tool execution, billing-impacting action, major provider boundary issue	immediate
High	successful prompt injection, sensitive trace exposure, approval bypass, provider route mismatch	same business day
Medium	blocked unsafe tool attempt, contained injection, trace policy deviation, answer drift	weekly incident review
Low	benign output defect, documentation mismatch, minor evidence freshness issue	monthly trend review

Response phases

Phase	Target	Required actions
Triage	first 30 minutes	classify, assign severity, identify affected route/source/tool/trace, freeze evidence
Containment	first 2 hours	disable affected source, route, prompt, tool, or provider path
Reconstruction	same business day	reconstruct prompt, retrieval, policy, output, tool, approval, and affected users
Remediation	incident-dependent	fix weakness, update tests, update release gate, update evidence
Communication	incident-dependent	prepare internal and customer-safe language

Findings

Incident Readiness Findings

Finding · high

Trace preservation must be explicit

Evidence: ai-trace-schema

AI traces may contain the only practical evidence for prompt, retrieval, model route, tool call, approval, and output reconstruction.

Finding · high

Customer notification triggers must cover AI-specific harm

Evidence: customer-notification-policy

Notification logic should explicitly cover unauthorized generated disclosure, unsafe AI-assisted action, and material changes to trust-center claims.

Finding · medium

AI incident response needs tabletop validation

Evidence: ai-incident-tabletop

The playbook should not be treated as mature until the team has run at least one RAG leakage scenario and one tool-action scenario.

Customer notification triggers

Trigger	Owner	Status
Customer data exposed to unauthorized user, tenant, provider route, or third party	Legal and Privacy	notify per policy
AI-assisted action caused customer-visible impact	Legal, Product Operations, Customer Success	case-by-case
Incident changes accuracy of questionnaire or trust-center claims	Trust and Security	update required

Tabletop scenarios

Checklist

Tabletop scenarios to run

✓Generated answer contains restricted support case detail.

✓Agent queues unsafe billing-impacting action.

✓Retrieved content successfully changes model behavior.

✓Provider route sends unexpected data class.

✓AI trace retention policy fails during investigation.

Artifact

Related artifact: AI Security Operating Model Blueprint

The operating model defines who owns AI incident readiness and review cadence.

/deliverables/ai-security-operating-model-blueprint

Artifact

Related artifact: AI Evidence Pack Appendix

The evidence appendix indexes the traces, screenshots, tests, and artifacts needed for investigation.

/deliverables/ai-evidence-pack-appendix