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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
Topic 2
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 3
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q132-Q137):

NEW QUESTION # 132
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

Answer: B

Explanation:
AAISM states that AI risk signals, thresholds, and model logic must be governed through strict validation and change control processes. Disabling key risk indicators without formal review or testing directly reflects a failure in:
- AI model validation
- Change management
- Governance oversight


NEW QUESTION # 133
What is the PRIMARY purpose of a dedicated AI management system policy?

Answer: A

Explanation:
AAISM states that an AI management system policy provides organizational structure by:
- defining AI objectives
- aligning governance
- outlining accountability
- defining roles, responsibilities, and guiding principles


NEW QUESTION # 134
Which approach should an organization prioritize to effectively verify the security of its AI models?

Answer: A

Explanation:
The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems.
Effective AI security testing requires:
* AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)
* Adversarial attack simulations (white-box, black-box, gradient-based attacks)
* Evaluation of robustness and manipulation resistance
Option B captures these requirements precisely.
Options A, C, and D do not address AI-specific attack vectors.
References: AAISM Study Guide - AI Security Testing and Adversarial Evaluation.


NEW QUESTION # 135
Which of the following is the MOST effective action an organization can take to address data security risk when using generative AI features in an application?

Answer: C

Explanation:
AAISM directs organizations to manage third-party AI risks through contractual and technical controls that explicitly govern data use, retention, training/fine-tuning, isolation, and deletion. The most effective data-security action when consuming generative AI features is to require enforceable opt-out provisions that prohibit the provider from using the organization's data for training or secondary purposes and that mandate retention limits and secure deletion.


NEW QUESTION # 136
A global organization has experienced multiple incidents of staff copying confidential data into public chatbots and acting on the model outputs. Which of the following is MOST important to reduce short-term risk when launching an AI security awareness initiative?

Answer: A

Explanation:
AAISM prescribes targeted, role-based, scenario-driven training aligned to policy and job tasks as the highest- impact near-term intervention for human-factor AI risks. By mapping concrete "do/don't" behaviors (e.g., what data may/may not be pasted into public chatbots, required redaction steps, approved tools, verification of outputs) to specific roles, organizations rapidly reduce incident likelihood and harmful actions.
* A (blocking) is a technical containment option but is not an awareness-initiative control and may cause workarounds; AAISM treats it as complementary, not a substitute for behavior change.
* B generic modules fail to address the specific misuse pattern.
* D signatures provide attestations without ensuring comprehension or changed behavior.
References:* AI Security Management™ (AAISM) Body of Knowledge: Human-centric Controls-Role- based training, policy-to-practice mapping, and scenario exercises for rapid risk reduction.* AI Security Management™ Study Guide: Awareness program design for generative AI misuse; behavior-anchored training outcomes.


NEW QUESTION # 137
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