AI incident response plan and resources
Description
Documented incident response playbooks specific to AI failure modes (prompt injection, model jailbreak, output bias, training data leakage) with assigned response owners.
⚠️ Risk Impact
Generic incident response runbooks don't cover AI-specific failure modes. When prompt injection or jailbreaking surfaces in production, the on-call engineer has no playbook — they improvise. Improvisation under pressure produces 4× longer time-to-contain (DORA AI Incident Report 2024).
🔍 How EchelonGraph Detects This
EchelonGraph's Tier 1 Cloud Scanner automatically checks for this condition across all connected cloud accounts. Violations are flagged as high-severity findings with remediation guidance.
🔧 Remediation
Author AI-specific runbooks: (1) prompt-injection response (rate-limit + sanitisation deployment), (2) output-bias surge (kill-switch + fairness re-eval), (3) training-data leak (egress block + model re-train), (4) jailbreak in production (system-prompt reinforcement + filter deployment). Rehearse quarterly.
💀 Real-World Attack Scenario
A customer-support LLM started outputting competitor product information after a prompt-injection campaign in a public Twitter thread. The on-call engineer had no playbook for 'AI output is wrong because of adversarial inputs'. The incident remained live for 8 hours while engineering improvised; the screenshot of the AI praising competitors went viral with 2M impressions.
💰 Cost of Non-Compliance
Generic IR vs AI-specific IR: 4× longer MTTC (DORA 2024). Brand impact of viral AI failure: avg $2.8M (Edelman Trust Barometer 2024).
📋 Audit Questions
- 1.Show me your prompt-injection response runbook.
- 2.When was the last AI-specific incident drill?
- 3.What is the kill-switch authority for your customer-facing AI?
- 4.How is an AI incident classified (serious vs minor) for EU AI Act Article 72 reporting?
🎯 MITRE ATT&CK Mapping
⚡ Common Pitfalls
- ⛔Adapting the network-incident runbook for AI without adding AI-specific steps
- ⛔Not running AI-incident drills — first execution is during a real incident
- ⛔Not having a documented kill-switch for AI services (the system can't be quickly disabled)
📈 Business Value
AI-specific runbooks + drills cut AI-incident MTTC from 8 hours to ~90 minutes — preventing the viral-screenshot scenario that consumes leadership attention for weeks.
⏱️ Effort Estimate
2-3 weeks for runbook authoring + 1 day per quarterly drill
EchelonGraph ships runbook templates; integrates kill-switch toggles into the dashboard
🔗 Cross-Framework References
Automate NIST AI-RMF MANAGE-2.1 compliance
EchelonGraph continuously monitors this control across all your cloud accounts.
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