ISACA AAISM Exam Questions [2026]

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:

Answer: D

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 # 63
When an attacker uses synthetic data to reverse engineer an organization's AI model, it is an example of which of the following types of attack?

Answer: C

Explanation:
AAISM defines model inversion attacks as those where adversaries use queries or synthetic data to reconstruct sensitive information or approximate the inner workings of a model. By exploiting outputs, attackers attempt to reverse engineer training data or model functionality. Distillation refers to compressing models, not adversarial attacks. Prompt attacks relate to manipulating language model inputs, and poisoning occurs when adversaries corrupt training data rather than infer from outputs. The scenario describes attackers using synthetic data to reveal hidden characteristics, which aligns directly with inversion attacks.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Attack Types and Mitigations) AI Security Management Study Guide - Model Inversion Risks


NEW QUESTION # 64
Which of the following actions BEST enables the evaluation of bias during an AI impact assessment?

Answer: A

Explanation:
In AI Security Management, bias evaluation is primarily a data problem before it is a model or performance problem. The official AI Security Management content explains that impact assessments must specifically analyze training data representativeness against the demographics and characteristics of all relevant users and stakeholders. If the data used to train an AI system underrepresents or omits particular groups, the resulting model will systematically disadvantage them, regardless of how fast or "accurate" it is on average. While comparing outputs to historical data (option B) can help, historical data itself may be biased. Performance- related options (C and D) relate to efficiency and scalability, not fairness. Therefore, systematically assessing whether the training data covers all relevant end-user groups is the most direct and effective way to evaluate and mitigate bias.
References: AI Security Management™ (AAISM) Study Guide - AI Risk Identification and Impact Assessment; Data Governance and Bias Section.


NEW QUESTION # 65
Which of the following information is MOST important to include in a centralized AI inventory?

Answer: A

Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.
References:
AAISM Exam Content Outline - AI Governance and Program Management (AI Inventories and Oversight) AI Security Management Study Guide - Ownership and Accountability Structures


NEW QUESTION # 66
Which of the following BEST describes the role of model cards in AI solutions?

Answer: D

Explanation:
AAISM positions model cards as standardized documentation artifacts that record intended use and out-of- scope use, training/evaluation data characteristics, performance metrics across groups, limitations/risks
, and governance controls/owners. Their purpose is transparency and assurance, not automated tuning or synthetic data generation. Visualization (A) may appear within a card, but the core role is structured documentation for governance, risk, and compliance.
References: AI Security Management (AAISM) Body of Knowledge - Documentation & Transparency Artifacts; Model Cards for Governance, Risk, and Assurance; Intended Use, Limitations, and Performance Disclosure.


NEW QUESTION # 67
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