AI Business Continuity and Incident Response
Governance also includes AI incident classification, response roles, evidence preservation, communications, recovery objectives, and continuity planning.
How to study for ISACA AAISM
Treat each item as a management decision: identify the AI asset and stakeholder, assess risk, select governance or control action, then document evidence and accountability.
Core concepts
Concept 1
AI incident response should classify events such as data leakage, model compromise, unsafe output, prompt abuse, service outage, and third-party failure.
Exam cue: Use incident response when the AI system is behaving unsafely or exposing data.
Concept 2
Continuity planning should define recovery priorities, manual workarounds, model rollback, endpoint failover, and communication responsibilities.
Exam cue: Use continuity planning when AI downtime affects business services.
Concept 3
Incident evidence should preserve prompts, logs, outputs, model versions, data sources, access records, and decision timelines.
Exam cue: Preserve AI-specific evidence before changing prompts, models, or retrieval data.
Risk pitfalls and guardrails
Using a generic incident plan that ignores prompts, models, and outputs.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Recovering service without validating safety and control state.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Failing to preserve the evidence needed for root cause and legal review.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Memory anchors
AI Incident
An AI incident is an event involving AI systems that threatens confidentiality, integrity, availability, safety, compliance, or trust.
Model Rollback
Model rollback returns a system to a previously approved model, prompt, data, or configuration version.
Evidence Preservation
Evidence preservation keeps prompts, logs, outputs, versions, access records, and timelines intact for investigation.
Recovery Objective
A recovery objective defines how quickly and how completely an AI-supported service must be restored.
Manual Workaround
A manual workaround keeps critical work moving when AI service is unavailable or unsafe.
Incident Classification
Incident classification groups AI events by severity, type, impact, and required response path.
Communication Plan
A communication plan defines who is notified, what is disclosed, and when during an AI incident.
Post-Incident Review
A post-incident review identifies root causes, control gaps, lessons learned, and remediation owners.
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 critical claims process depends on a model API, retrieval index, identity service, and provider moderation, but the BIA lists only the application. Which control is MOST appropriate?
Which evidence BEST demonstrates that controls over business impact analysis operated throughout the review period?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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