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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which of the following is the GREATEST risk of using AI to generate audit reports?

Answer: D

Explanation:
The greatest risk when using AI to generate audit reports is that it may misrepresent control effectiveness (B)--for example, by overstating the robustness of controls, understating deficiencies, or summarizing findings inaccurately. This undermines the reliability of the entire audit and can lead to poor management decisions, regulatory issues, and reputational damage.
AAIA emphasizes that AI-generated artifacts still require professional judgment and validation to ensure they accurately reflect audit evidence and conclusions.


NEW QUESTION # 84
Which of the following is an IS auditor's MOST important course of action when determining whether source data should be entered into approved generative AI tools to assist with an audit?

Answer: A

Explanation:
When using generative AI tools during audit activities, the most critical concern is the reliability and appropriateness of the information being entered and processed. According to the AAIATM Study Guide, auditors are accountable for ensuring that audit data is valid, confidential, and that generated outputs are factual and verifiable.
"IS auditors must evaluate whether the information entered into AI tools is reliable and appropriate for the audit context. Inputting sensitive or unverified data may lead to regulatory violations or audit inaccuracies."


NEW QUESTION # 85
During an audit of a bank ' s AI credit scoring system, an IS auditor discovers that applicants were not informed about automated decision-making. Which of the following should the auditor do FIRST?

Answer: A

Explanation:
Transparency is a fundamental legal and ethical requirement for AI systems, particularly under regulations like GDPR, which mandate that data subjects be informed of automated decision-making. If an auditor finds that applicants were not informed, the immediate " First " step is to " Evaluate transparency controls " to determine why the notification process failed and to assess the scope of the non-compliance. This includes reviewing user agreements, privacy notices, and communication procedures. Once the failure is understood and the risk assessed, the auditor can move on to evaluating the appeal process (Option C) or preparing the final report (Option B).


NEW QUESTION # 86
An organization's system development process has been enhanced with AI. Which of the following features presents the GREATEST risk?

Answer: B

Explanation:
Allowing AI to autonomously generate code without human review introduces significant risks, including security vulnerabilities, logic errors, and noncompliance with organizational development standards. The AAIATM Study Guide strongly advocates for human-in-the-loop oversight, particularly in automated development contexts.
"AI-assisted development must include manual code reviews to ensure functionality, compliance, and security. Autonomous code generation without validation increases the risk of introducing undetected flaws."


NEW QUESTION # 87
Which of the following is the PRIMARY objective of performing adversarial testing on AI models?

Answer: A

Explanation:
Adversarial testinginvolves simulating real-world attacks or malicious inputs against AI models (e.g., adversarial examples, poisoning, evasion) to identify how the system behaves under intentional misuse or hostile conditions. Theprimary objectiveis to discoverweaknesses and control gaps(D) in the model and its surrounding processes-such as inadequate input validation, insufficient monitoring, or missing safeguards against adversarial inputs.
While results from adversarial testing may inform incident response planning (A), KRI definition (B), or security awareness (C), those are secondary benefits. AAIA's coverage of AI threats and vulnerabilities emphasizes adversarial testing as acontrol validation and gap-identification mechanism, directly addressing AI-specific risk exposure.
References:
ISACA,AAIA Exam Content Outline- Domain 1 and Domain 2: Threats and Vulnerabilities Specific to AI; Testing Techniques for AI Solutions.
ISACA guidance on adversarial testing and AI security posture assessment.


NEW QUESTION # 88
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