Originally published at https://charz.ai/blog/ai-incident-response-playbook-template by Char-Z AI.
Why AI Needs Its Own Incident Response
AI incidents are not exactly like security incidents. They include novel failure classes — harmful or unsafe output, bias, confabulation, model drift, prompt injection, and data leakage through a trained model — that a conventional incident response plan does not cover (NIST, 2024). An AI-specific playbook makes sure the right people act on the right triggers, fast, and that the notification duties that can follow (GDPR 72-hour breach notification, for example) are met (European Commission, 2016).
This template is a starting point. Adapt the triggers, roles, and severities to your organization and your systems.
Severity Levels
This template is a starting point. Adapt the triggers, roles, and severities to your organization and your systems.
L3 — Low — no user impact (e.g. minor output drift) — response: log + monitor.
L2 — Medium — limited impact, contained (e.g. harmful output to a few users) — investigate + mitigate.
L1 — High — significant harm or exposure (e.g. data breach, exploitable flaw) — full response + possibly notify.
The Playbook Structure
1. Roles and contact
- Incident commander (owns the response)
- AI/system owner (technical)
- Data-protection / privacy lead
- Legal / compliance
- Communications (for external notice)
Fill in names and an on-call rotation. Pre-assign who decides severity escalation.
2. Detection triggers
- Harmful, unsafe, or biased output reported
- Data leakage or unexpected data egress
- Confabulation causing consequential harm
- Model drift or performance degradation
- Prompt injection or adversarial attack
- Vendor incident notification (see our vendor incident response guide)
- Regulatory inquiry or complaint
3. Response steps (by phase)
Detect
- Confirm the incident and log it.
- Record what the AI did, the inputs/outputs, and the impact window.
Contain
- Disable or isolate the affected system or feature.
Suspend data flow into the system if data exposure is possible (data-
flow mapping tells you what to cut).Revoke exposed credentials or keys.
Assess
- Determine data involved and its sensitivity/tier.
Determine whether any personal data was exposed or processed
improperly.Determine legal or regulatory notification obligations.
Notify
- Use your pre-agreed notification template.
Include the 72-hour GDPR window for qualifying personal-data breaches
(European Commission, 2016).Notify the vendor and request their findings where the incident is third-party.
Preserve evidence
- Keep logs, outputs, and communications for the incident file and any audit.
Restore + monitor
- Restore with mitigations in place.
- Watch for recurrence and reassess the system before full return.
Review
- Document root cause and corrective actions.
- Feed lessons into the risk register and vendor reviews.
Fast-Reference Card
On any AI incident:
Log it and assign the commander.
Contain: isolate the system and cut data flow.
Assess data exposure + sensitivity.
Decide notification (72h GDPR window).
Preserve evidence.
Restore with mitigations, then review.
Making It Real
- Drill it. Run a tabletop exercise once or twice a year.
Keep contacts current. Outdated on-call lists are the most common
failure.Link it to governance. The NIST AI RMF's Manage function treats incident response and post-incident monitoring as continuous obligations, not one-time steps (NIST, 2023).
Sources
NIST. (2024). *Generative Artificial Intelligence Profile (NIST AI 600-1)*. National Institute of Standards and Technology.
NIST. (2023). *Artificial Intelligence Risk Management Framework (AI RMF 1.0)*. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
European Commission. (2016). Regulation (EU) 2016/679 — General Data Protection Regulation. *Official Journal of the European Union*.
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