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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 (Q371-Q376):

NEW QUESTION # 371
Which of the following is the MOST critical success factor for an AI implementation project?

A. Ensuring AI risk is captured in the risk register
B. Mapping data throughout the life cycle
C. Obtaining senior management buy-in
D. Developing and using model cards

Answer: C

Explanation:
AAISM identifies executive sponsorship and senior management buy-in as the foremost success factor for AI initiatives. It secures resources, resolves cross-functional conflicts, sets risk appetite, and enforces adherence to governance and controls. Model cards (A), risk registers (B), and lifecycle data mapping (C) are vital practices within the program, but without top-level commitment, adoption, funding, and accountability often fail.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Program Governance; Executive Sponsorship & Accountability; Strategy-to-Control Alignment for Successful AI Delivery.

NEW QUESTION # 372
Which of the following BEST addresses risk associated with hallucinations in AI systems?

A. Automated output validation
B. Recursive chunking
C. Content enrichment
D. Human oversight

Answer: D

Explanation:
AAISM prescribes human-in-the-loop (HITL) controls as the primary safeguard for high-impact generative AI use cases to mitigate hallucination risk. Human oversight ensures critical outputs are reviewed, corrected, and approved before use, with accountability, escalation, and documented decision trails. Automated validators and enrichment help reduce errors but are secondary; recursive chunking is a prompting tactic, not a governance control.
References: AI Security Management (AAISM) Body of Knowledge: Responsible AI & Human Oversight; Generative AI Risk Controls-Approval Workflows and Human Review; AAISM Study Guide: Hallucination Risk Treatment with HITL and Approval Gates.

NEW QUESTION # 373
When addressing privacy concerns related to AI systems, which of the following is the GREATEST significance of user consent for an organization?

A. It allows the organization to process user data in the AI system
B. It enables users to delete and modify their personal data
C. It prevents unauthorized access to data within the AI system
D. It helps the organization detect biases and ensure fairness

Answer: A

Explanation:
Within AAISM's privacy governance, consent is a lawful basis that authorizes processing of personal data for defined purposes. Its principal significance is granting the organization the authority to process user data in AI workflows in line with stated purposes and limits.

NEW QUESTION # 374
An organization is conducting an AI risk assessment and wants to establish risk thresholds for adversarial threats such as evasion attacks and data poisoning. Which of the following is MOST important to be approved by senior management?

A. The backup policy to prevent loss of data
B. The acceptable level of model performance degradation
C. The AI training plan focusing on human safety
D. The extent of human-in-the-loop oversight

Answer: B

Explanation:
Senior management should approve the acceptable level of model performance degradation because it defines the organization's risk tolerance for adversarial threats and operational impact.
Establishing these thresholds is a governance decision that directly affects business risk acceptance and security posture.

NEW QUESTION # 375
The BEST reason for inviting independent reviewers to evaluate the output of an AI application is to:

A. improve accountability.
B. mitigate internal bias.
C. enhance governance.
D. improve transparency.

Answer: B

Explanation:
Independent reviewers provide an external perspective that helps identify and reduce biases that internal teams may overlook due to familiarity or shared assumptions, leading to more objective evaluation of AI outputs.

NEW QUESTION # 376
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