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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 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.
Topic 3
  • 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
The MOST important consideration when establishing key risk indicators (KRIs) to monitor AI model drift in an operational machine learning (ML) system is adopting KRIs that:

Answer: C

Explanation:
AI model drift occurs when a model's performance or behavior changes over time due to evolving data patterns or environments. KRIs that identify variations in model accuracy and outputs are the most important because they directly indicate whether the model is deviating from expected performance and reliability.


NEW QUESTION # 31
Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?

Answer: D

Explanation:
A confusion matrix is explicitly defined in AAISM as the framework used to interpret classification performance by listing:
* true positives
* true negatives
* false positives
* false negatives
Precision (B) and recall (D) are derived metrics that use parts of the matrix but do not show the full picture.
Hyperparameter tuning (A) is unrelated.
References: AAISM Study Guide - AI Model Evaluation Metrics; Confusion Matrix.


NEW QUESTION # 32
Which of the following is the MOST important consideration when deciding how to compose an AI red team?

Answer: B

Explanation:
AAISM materials specify that the composition of an AI red team must be tailored to the organization's AI use cases. The purpose of red-teaming is to simulate realistic adversarial conditions aligned with the actual applications of AI. For example, testing a generative model requires different expertise than testing a fraud detection system. While resource availability, compliance requirements, and time-to-market pressures are practical considerations, they are secondary to aligning team expertise with use case scenarios. The most important factor is therefore the AI use cases themselves.


NEW QUESTION # 33
When robust input controls cannot prevent prompt injections in an LLM, what is the BEST compensating control?

Answer: D

Explanation:
AAISM identifies output review and annotation as the most practical compensating control when robust input validation cannot be applied.
Output moderation detects:
- maliciously influenced responses
- unsafe outputs
- security-policy violations


NEW QUESTION # 34
Which of the following is MOST important to consider when obtaining senior management and stakeholder support for a new AI governance program?

Answer: B

Explanation:
Senior management and stakeholders are most influenced by how an AI governance program affects organizational objectives, operations, risk management, compliance, and business processes. Demonstrating enterprise-wide impact helps secure strategic alignment, sponsorship, and long-term support for the governance initiative.


NEW QUESTION # 35
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