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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 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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q259-Q264):

NEW QUESTION # 259
Before using data from public domain sources to train generative AI models, it is MOST important to verify that the data source site:

Answer: C

Explanation:
Verifying that the site demonstrates ownership ensures the data is legitimately controlled and legally usable, reducing risks of copyright infringement and unauthorized use when training generative AI models.


NEW QUESTION # 260
The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:

Answer: A

Explanation:
An AI architecture, within program governance, exists to align AI system components and lifecycle processes with business goals and policy constraints. Architecture provides the organizing structure linking strategy, capabilities, processes, data, models, controls, and assurance so that AI outcomes are traceable to business value, risk appetite, and compliance expectations. Efficiency, speed, and threat analysis are important architectural qualities, but they are not the primary purpose; the primary purpose is strategic and governance alignment so that technical choices and controls consistently realize organizational objectives.
References:* AI Security Management (AAISM) Body of Knowledge: AI Program Architecture - alignment of capabilities, processes, and controls to business objectives* AI Security Management Study Guide: Architecture-driven governance, traceability from business goals to technical and control design


NEW QUESTION # 261
An organization recently introduced a generative AI chatbot that can interact with users and answer their queries. Which of the following would BEST mitigate hallucination risk identified by the risk team?

Answer: C

Explanation:
AAISM highlights fine-tuning foundational models as one of the most effective strategies for reducing hallucination risk. By tailoring the model with domain-specific, curated, and verified datasets, organizations can reduce the frequency of irrelevant or fabricated outputs. Testing and validation help evaluate risks but do not directly minimize hallucinations. Training on larger datasets may improve generalization but does not guarantee accuracy. Developer training in AI risk supports governance but is not a technical control against hallucinations. The best mitigation is fine-tuning to align the chatbot with trusted, context-specific knowledge.


NEW QUESTION # 262
Which of the following should be the PRIMARY consideration for an organization concerned about liabilities associated with unforeseen behavior from agentic AI systems?

Answer: D

Explanation:
AAISM governance guidance stresses that when dealing with agentic AI systems capable of autonomous decision-making, the primary consideration is accountability. Without clear accountability structures, unforeseen or harmful outcomes may result in unmitigated liability for the organization. While dependencies, base models, and defined risk levels are important, they do not directly address who is responsible when systems act unpredictably. The key governance safeguard is the implementation of an accountability model that ensures liability and oversight are properly assigned.


NEW QUESTION # 263
An organization's CIO provided the AI steering committee with a list of AI technologies in use and tasked them with categorizing the technologies by risk. Which of the following should the committee do FIRST?

Answer: A

Explanation:
AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance. Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.
References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories as a Prerequisite to Risk Analysis) ISACA AI Security Management - Asset Visibility and Risk Categorization


NEW QUESTION # 264
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