AI Incident Response: Playbooks for Prompt Injection, Model Abuse, Data Leakage, and Rogue Agents

Slug: ai-incident-response-playbooks Effective Date: 2026-05-17 Version: v1.0 Author: Alex Eisen Status: Draft Minimum Target Length: 2,000 words

AI incidents are often hard to triage because the failure starts in one layer and ends in another. The playbook has to answer four questions quickly: what did the model see, what did it do, what authority did it have, and what data may have left the boundary?

1. Why This Matters

Most incident teams already know how to isolate systems and preserve logs. AI changes the shape of the evidence. The response process must include prompts, retrieval context, tool actions, and model versions.

2. Core Concept

The playbook should focus on containment, replay, and decision speed. The goal is not to produce a perfect root-cause story before acting. The goal is to stop the blast radius and preserve the trail.

3. Threat Model or Failure Model

• Prompt injection drives an unexpected action path.
• The model leaks private data into an output or external tool.
• A rogue agent keeps acting after the intended workflow ended.
• The team loses evidence because the response plan did not preserve AI-specific telemetry.

4. Framework Mapping

Use standard incident response structure, then add AI-specific artifacts and decision points. OWASP and ATLAS help with threat classes. NIST AI RMF keeps the governance story straight.

5. Engineering Controls

• Define containment steps for model, retrieval, and tool layers.
• Preserve prompts, retrieval logs, approvals, and output traces.
• Document the rollback or disable path for each high-risk workflow.
• Run tabletop exercises that include AI-specific evidence collection.

6. Tooling

• Use trace stores, alert pipelines, and replayable logs.
• Keep incident artifacts separate from normal product telemetry.
• Make the kill switch and disable path easy to find under pressure.

7. Evidence and Observability

• Evidence should show the attack path and the containment action.
• Record who made the decision and why.
• Keep the incident packet small enough for a fast review.

8. Operating Model

The incident commander owns the response, but platform engineering owns the disable path and security owns the evidence model. If the team has to invent the process during the incident, the playbook was never real.

9. Common Mistakes

• Waiting to understand everything before containing the issue.
• Forgetting to preserve prompts and retrieval context.
• Leaving the kill switch undocumented.
• Writing the playbook after the incident.

10. Practical Example

A customer-facing assistant leaks a private policy fragment through a support answer. The response plan should isolate the workflow, preserve the retrieval trail, and show whether the leak came from a prompt, a document, or a permission gap.

11. Governance and Claim Caveats

• Sponsor support does not influence methodology, scoring, findings, chart outputs, or editorial conclusions.
• Job-description intelligence and public hiring signals are directional signals, not proof of internal security maturity.
• Psychometric outputs are role-language evidence, not diagnosis.
• Avoid accusatory company-level language.
• Avoid product endorsement language.

12. Conclusion

AI incident response is good when it is boring under pressure. The playbook should make the right actions obvious before the incident starts.

Implementation Checklist

• Define containment steps.
• Preserve AI telemetry.
• Document the kill switch.
• Run a tabletop.
• Track the decision owner.
• Keep the evidence packet.
• Retest the disable path.
• Update the runbook.
• Separate facts from assumptions.
• Review the public language.

Source Notes Needed

• Incident response runbooks.
• OWASP LLM guidance.
• MITRE ATLAS.
• NIST AI RMF.
• Public AI incident reports.