Privacy, Ethics, Trust and Safety Controls
AAISM control design includes privacy, transparency, explainability, fairness, human oversight, misuse prevention, content safety, and trust signals.
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
Privacy and ethics controls should be designed into AI requirements rather than added only after incidents.
Exam cue: Use human oversight when AI decisions have high impact or low tolerance for error.
Concept 2
Transparency and explainability should be appropriate to user impact, regulatory expectations, and decision significance.
Exam cue: Use transparency when users need to understand AI involvement or limitations.
Concept 3
Human oversight should be used when AI outputs affect rights, safety, high-impact decisions, or sensitive operations.
Exam cue: Use safety controls when outputs could harm people, operations, compliance, or trust.
Risk pitfalls and guardrails
Equating fairness with a single metric without context.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Letting opaque AI outputs drive high-impact decisions without review.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Treating ethics as optional communications language rather than control design.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Memory anchors
Human Oversight
Human oversight adds review, approval, escalation, or intervention for high-impact AI decisions.
Transparency
Transparency tells stakeholders when AI is used, what it does, and what its limits are.
Explainability
Explainability helps users or reviewers understand why an AI output or decision occurred.
Fairness Control
A fairness control tests and mitigates unjustified differences in AI outcomes across groups.
Content Safety
Content safety controls block or route harmful, abusive, illegal, or policy-violating outputs.
Misuse Prevention
Misuse prevention limits ways an AI system can be abused outside its approved purpose.
Trust Signal
A trust signal gives users evidence about source, confidence, review, limitation, or provenance.
Ethical Requirement
An ethical requirement translates organizational values into concrete AI design or control expectations.
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
An inventory model is repurposed to predict employee absence without a new privacy review. Which control is MOST appropriate?
Which evidence BEST demonstrates that controls over privacy impact assessment operated throughout the review period?
Answer all questions to submit.
Next step personalized recommendations
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Move forward only after this module is stable.
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