AI Security Strategies, Policies and Procedures
This topic covers AI security strategy, policy hierarchy, acceptable use, development requirements, procedures, exception handling, and governance evidence.
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
An AI security strategy aligns AI adoption, control priorities, monitoring, investment, and risk tolerance with enterprise objectives.
Exam cue: Use policy when the organization needs mandatory governance direction.
Concept 2
Policies establish mandatory expectations; standards define required criteria; procedures explain repeatable operating steps.
Exam cue: Use procedures when operators need repeatable steps.
Concept 3
Exception management documents temporary deviations, approvals, compensating controls, expiration, and review.
Exam cue: Use exception management when a control cannot be met temporarily.
Risk pitfalls and guardrails
Writing high-level principles without enforceable standards.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Approving exceptions without expiration or compensating controls.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Letting AI teams create local rules outside enterprise policy.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Memory anchors
AI Security Strategy
An AI security strategy connects AI adoption goals to risk priorities, control investment, monitoring, and governance outcomes.
Policy
A policy states mandatory AI security expectations approved by management.
Standard
A standard defines specific required criteria that support a policy.
Procedure
A procedure gives repeatable steps for operating, testing, or documenting an AI security process.
Acceptable Use
Acceptable use rules define permitted and prohibited AI activities, data inputs, outputs, and user behaviors.
Exception
An exception is an approved temporary deviation from a control requirement.
Compensating Control
A compensating control reduces risk when the primary required control cannot be implemented.
Policy Evidence
Policy evidence proves that governance expectations were communicated, implemented, reviewed, and enforced.
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
The enterprise AI policy covers internally developed models but excludes embedded AI features and employee-selected tools. Which control is MOST appropriate?
Which evidence BEST demonstrates that controls over policy scope operated throughout the review period?
Answer all questions to submit.
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Move forward only after this module is stable.
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