AI system incidents are not the same shape as network or endpoint incidents. Prompt injection, model substitution, tool-call hijacking, data leakage via completions, fine-tune backdoors — each has specific investigation and containment patterns. Generic IR playbooks don't cover them. Enterprise AI deployments need dedicated AI-IR playbooks that run alongside traditional ones.
What AI incidents look like
Six common patterns:
- Prompt injection via indirect content.
- Model substitution at the network or proxy layer.
- Tool-call scope escape.
- Data leakage via completion.
- RAG poisoning.
- Fine-tune backdoor activation.
Each has specific signatures and specific response.
Playbook structure
Five sections per playbook:
- Detection signals. What triggers the playbook.
- Containment steps. Immediate actions to stop harm.
- Investigation paths. Evidence to collect, questions to answer.
- Eradication. Remove the root cause.
- Communication. Internal and external messaging.
Each section has AI-specific content that differs from traditional IR.
Example: prompt injection playbook
- Detection: anomalous tool-call pattern; specific IoC strings in audit logs.
- Containment: revoke session; disable affected workflow.
- Investigation: trace the injected content to its source; identify the ingest path.
- Eradication: remove source; purge cache; update ingest rules.
- Communication: notify affected users; file with vendor if their platform was abused.
How Safeguard Helps
Safeguard ships pre-built AI-IR playbooks covering the common incident classes. Customers adopt and adapt rather than write from scratch. For organisations whose traditional IR covers the network and endpoint but not AI-specific failures, this closes the playbook gap.