AI Incident Response, Resilience and Continuity
This topic covers AI incident classification, evidence preservation, communications, rollback, recovery, continuity plans, and resilience testing.
How to study for ISACA AAIA
Treat each item as an audit decision: understand AI risk, identify criteria, test evidence, assess control effectiveness, then report impact and follow-up.
Core concepts
Concept 1
AI incidents can involve data leakage, prompt abuse, unsafe output, model compromise, vendor outage, tool misuse, drift, or service failure.
Exam cue: Preserve AI-specific evidence before changing prompts, models, or retrieval data.
Concept 2
Incident response should preserve prompts, outputs, logs, model versions, data sources, tool calls, access records, and decisions.
Exam cue: Use rollback when a model, prompt, or data change causes unsafe behavior.
Concept 3
Continuity planning should define recovery objectives, fallbacks, manual workarounds, failover, communications, and validation after recovery.
Exam cue: Test continuity for business processes that depend on AI services.
Risk pitfalls and guardrails
Using a generic incident plan that ignores prompts, tools, and outputs.
Guardrail: Avoid treating AI audit as only a technical scan, accepting management assertions without evidence, or omitting prompts, data, vendors, and monitoring.
Restoring service without validating safe AI behavior.
Guardrail: Avoid treating AI audit as only a technical scan, accepting management assertions without evidence, or omitting prompts, data, vendors, and monitoring.
Missing vendor notification duties during provider incidents.
Guardrail: Avoid treating AI audit as only a technical scan, accepting management assertions without evidence, or omitting prompts, data, vendors, and monitoring.
Memory anchors
AI Incident
An AI incident threatens confidentiality, integrity, availability, safety, compliance, fairness, or trust in AI use.
Evidence Preservation
Evidence preservation keeps prompts, outputs, logs, versions, tool calls, and timelines intact.
Rollback
Rollback returns a model, prompt, data, or configuration to a previously approved state.
Recovery Objective
A recovery objective defines how quickly and completely an AI-supported service must be restored.
Manual Workaround
A manual workaround keeps critical work moving when AI output is unavailable or unsafe.
Communication Plan
A communication plan defines who is notified, what is disclosed, and when during an AI incident.
Resilience Test
A resilience test verifies failover, recovery, manual workarounds, and dependency handling.
Post-Incident Review
A post-incident review identifies root cause, control gaps, lessons learned, and remediation.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
A chatbot begins exposing confidential text after a prompt update. What should incident responders do FIRST?
Which artifact is MOST important for reconstructing an AI agent's unauthorized payment?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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