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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q195-Q200):

NEW QUESTION # 195
Which of the following is the BEST reason to involve a diverse team when evaluating the data used within an AI system?

Answer: D

Explanation:
Involving a diverse team provides broader perspectives, experiences, and expertise during evaluation of AI data. This helps identify hidden biases, ethical concerns, security issues, and other risks that might otherwise be overlooked by a less diverse review group.


NEW QUESTION # 196
Which of the following would MOST effectively obtain ongoing support from stakeholders to align AI initiatives with business objectives?

Answer: D

Explanation:
Sustained stakeholder sponsorship hinges on demonstrated, quantified business value communicated in terms they own (KPIs, ROI, cost-to-serve, risk-adjusted outcomes). AAISM frames stakeholder alignment as a value-assurance loop: define value hypotheses, measure realized value, and continuously communicate results to sponsors. While an AI roadmap (C), risk optimization (B), and training (A) are important, they support rather than drive ongoing executive buy-in. Quantified value narratives secure resources and reinforce alignment to strategic goals.
References:* AI Security Management (AAISM) Body of Knowledge: Strategy & Value Realization- value metrics, benefits tracking, stakeholder reporting* AAISM Study Guide: Business alignment for AI- OKRs/KPIs, ROI cases, benefits realization management


NEW QUESTION # 197
What BEST protects trade secrets related to AI technologies during their life cycle?

Answer: D

Explanation:
AAISM emphasizes access control and data security as the strongest mechanisms to protect trade secrets, including:
- proprietary algorithms
- training datasets
- model weights
- internal design documentation


NEW QUESTION # 198
An organization is commissioning a third-party AI system using sensitive data. Which metric is MOST important to consider?

Answer: A

Explanation:
AAISM specifies that when AI systems process sensitive or high-impact data (e.g., financial, identity, health), accuracy thresholds become critical risk indicators. Inaccurate predictions can cause harm, regulatory non- compliance, discrimination, or financial loss.
Availability (D) is important but secondary to correctness in sensitive decision scenarios. Response time (B) and accessibility ratings (A) do not outweigh the need for accurate, reliable predictions.
References: AAISM Study Guide - AI Model Risk Metrics; Accuracy Requirements for High-Impact Systems.


NEW QUESTION # 199
In an AI governance framework, the MOST important reason to conduct periodic model validation is to:

Answer: A

Explanation:
Periodic model validation is essential to identify degradation in model performance, drift from expected behavior, and emerging security vulnerabilities over time. Continuous validation helps ensure the AI system remains accurate, reliable, secure, and aligned with operational and governance requirements.


NEW QUESTION # 200
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