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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 (Q44-Q49):

NEW QUESTION # 44
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?

A. Recall
B. Specificity
C. Precision
D. F1 score

Answer: A

Explanation:
AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.
References:
AAISM Study Guide - AI Technologies and Controls (Evaluation Metrics and Model Performance) ISACA AI Security Management - Model Accuracy and Completeness Assessments

NEW QUESTION # 45
In a new supply chain management system, AI models used by participating parties are interactively connected to generate advice in support of management decision making. Which of the following is the GREATEST challenge related to this architecture?

A. Identifying hallucinations returned by AI models
B. Establishing clear lines of responsibility for AI model outputs
C. Determining the aggregate risk of the system
D. Explaining the overall benefit of the system to stakeholders

Answer: B

Explanation:
The AAISM governance framework notes that in multi-party AI ecosystems, the greatest challenge is ensuring clear accountability for AI outputs. When models from different parties interact, responsibility for errors, bias, or harmful recommendations can be unclear, leading to disputes and compliance gaps. While aggregate risk assessment and error identification are significant, they are secondary to the fundamental governance requirement of establishing transparent lines of responsibility. Without defined accountability, no stakeholder can reliably manage or mitigate risks. Therefore, the greatest challenge in such a distributed architecture is responsibility for AI outputs.
References:
AAISM Study Guide - AI Governance and Program Management (Accountability in Multi-Party Systems) ISACA AI Governance Guidance - Roles and Responsibilities in AI Collaboration

NEW QUESTION # 46
An organization is facing a deepfake attack intended to manipulate stock prices. The organization's crisis communication plan has been activated. Which of the following is MOST important to include in the initial response?

A. Engage with brand monitoring services to track social media activity
B. Conduct a detailed forensic analysis to identify the source of the deepfake
C. Provide clarifying information in a pre-approved public statement
D. Conduct employee awareness training on recognizing deepfake videos and audio

Answer: C

Explanation:
AAISM guidance on crisis management and communication emphasizes that the initial priority in responding to a reputational or market manipulation attack is to provide accurate clarifying information to the public through a pre-approved statement. This ensures stakeholders and markets are given verified facts immediately, limiting the spread of misinformation. While forensic analysis, employee training, and monitoring activities are important, they occur after the immediate need for public trust and damage control is addressed. Pre-approved statements are a central control in AI-related incident response to ensure consistency, timeliness, and credibility in communications.
References:
AAISM Study Guide - AI Governance and Program Management (Incident Response and Crisis Communication) ISACA AI Security Management - Public Communication and Trust Preservation

NEW QUESTION # 47
Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?

A. Consolidate event logs for correlation and centralized analysis.
B. Deploy pre-trained models directly into production.
C. Schedule periodic manual code reviews.
D. Implement compartmentalization with least privilege enforcement.

Answer: D

Explanation:
The most effective strategy tominimize attack surfacesin AI agent security is to apply compartmentalization and least privilege enforcement.
AAISM control frameworks emphasize:
* Isolation of components (e.g., training, inference, data pipelines) to limit lateral movement.
* Principle ofleast privilegeto restrict access only to what is required for function.
* Hardening AI pipelines through segmentation rather than relying solely on manual reviews or monitoring.
Pre-trained models and log centralization are useful but do not directly reduce the attack surface.Manual code reviewsare important but insufficient against runtime exploitation.
Thus,compartmentalization with least privilege enforcementis the most effective technical safeguard.

NEW QUESTION # 48
Which of the following should be done FIRST when developing an acceptable use policy for generative AI?

A. Review AI regulatory requirements
B. Consult with risk management and legal
C. Determine the scope and intended use of AI
D. Review existing company policies

Answer: C

Explanation:
According to the AAISM framework, the first step in drafting an acceptable use policy is defining the scope and intended use of the AI system. This ensures that governance, regulatory considerations, risk assessments, and alignment with organizational policies are all tailored to the specific applications and functions the AI will serve. Once scope and intended use are clearly defined, legal, regulatory, and risk considerations can be systematically applied. Without this step, policies risk being generic and misaligned with business objectives.
References:
AAISM Study Guide - AI Governance and Program Management (Policy Development Lifecycle) ISACA AI Governance Guidance - Defining Scope and Use Priorities

NEW QUESTION # 49
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