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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
Topic 2	AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.
Topic 3	Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.

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ISACA Advanced in AI Audit Sample Questions (Q275-Q280):

NEW QUESTION # 275
Which of the following is the GREATEST concern when an audit team relies on generative AI to create audit reports?

A. The reports may tend to use generic language for audit issues.
B. The reports may be more likely to reflect outdated information.
C. The reports may contain misstatements resulting from hallucinations.
D. The reports may use inconsistent formatting from prior audit findings.

Answer: C

Explanation:
The greatest concern is that the generative model may hallucinate, producing incorrect facts or conclusions (option B). In an audit context, hallucinations can create false statements about control effectiveness, misreport risks, or incorrectly summarize evidence.
AAIA stresses that auditors must maintain professional skepticism and validate AI-generated content.
Misstatements are high-risk because they undermine audit credibility, regulatory compliance, and organizational decision-making.
Formatting inconsistency (C) and generic language (D) are cosmetic issues. Outdated information (A) is a concern but does not inherently create false conclusions.
Hallucinated misinformation is the most severe and dangerous issue in AI-generated audit reporting.
References:
AAIA Domain 3: AI in Audit Processes (accuracy of AI outputs, hallucination risks).
AAIA Domain 5: Ethical Responsibilities in AI-Assisted Work.

NEW QUESTION # 276
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?

A. Hiring a marketing firm to text links to customers requesting product reviews for monetary compensation
B. Peer reviewing code that acquires product reviews from social media posts
C. Augmenting the unbalanced product review data set with the use of oversampling by the model developer
D. Requiring customers to authenticate access to their accounts prior to writing product reviews

Answer: D

Explanation:
To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIATM Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.
"Limiting review input to authenticated users restricts unauthorized actors--such as competitors or bots--from submitting biased or harmful data. This control protects model training and outputs from being manipulated."

NEW QUESTION # 277
During an audit of an AI incident response plan, what is the MOST important element specific to AI (beyond a standard IT incident plan)?

A. A designated incident commander
B. A communication plan template
C. A post-incident report template
D. Procedures for identifying and containing model-specific failures such as drift, bias incidents, or data poisoning

Answer: D

Explanation:
Generic IT incident plans do not typically address AI-specific failure modes; a mature plan must explicitly define detection and containment procedures for issues like drift, poisoning, or biased outputs.

NEW QUESTION # 278
Which of the following represents the PRIMARY benefit of reviewing model cards during AI model acquisition and risk assessment?

A. Identifying model vulnerabilities and training needs
B. Understanding model intent and performance context
C. Establishing legally binding contracts for model use
D. Complying with documentation requirements

Answer: B

Explanation:
A "Model Card" is a standardized document that provides essential information about an AI model's intended use, training data, limitations, and performance metrics. For an auditor or risk manager, the primary benefit is gaining a clear "Understanding of model intent and performance context." It allows the organization to determine if a vendor's model is fit for the specific business purpose and to identify potential risks (such as data bias or environmental limitations) before acquisition.

NEW QUESTION # 279
Which of the following should be done FIRST when an attacker exfiltrates sensitive information from an AI model?

A. Inform regulators and affected stakeholders of a potential data breach.
B. Implement rate limiting and query restrictions to reduce exploitation attempts.
C. Isolate impacted systems until the attack vector is identified.
D. Rebuild the AI model using a more secure architecture.

Answer: C

Explanation:
According to the AAIA™ Study Guide, the first action in response to a confirmed or suspected data exfiltration attack should be containment. Isolating impacted systems helps prevent further exploitation while allowing for a secure investigation of the breach source.
"The initial response to any AI-related data breach must prioritize containment of the threat. Immediate isolation of affected systems helps mitigate further damage and supports a controlled forensic analysis." While regulatory notification (D) and architectural remediation (C) are important, they follow containment.
Query limitations (A) reduce future risk but do not address the current attack. Thus, B is the critical first step.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "AI Security Incident Response and Containment"

NEW QUESTION # 280
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