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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 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 3
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q59-Q64):

NEW QUESTION # 59
An organization using AI to create digital content faces challenges in protecting its intellectual property. Which of the following is the BEST way to mitigate this risk?

Answer: A

Explanation:
For AI-generated content (images, text, or audio), "watermarking" is a critical technical control to protect Intellectual Property (IP) and ensure provenance. Digital watermarks can be embedded into the metadata or the content itself (often invisibly) to prove the organization is the creator and to track unauthorized distribution. This supports legal claims and brand protection. Firewalls and data separation protect the system, but watermarking protects the output once it is shared online or with clients. As AI makes content creation easier, the ability to prove ownership becomes an essential ethical and legal safeguard.


NEW QUESTION # 60
An IS auditor is reviewing AI-driven processes that automate financial approvals. Which of the following poses the GREATEST challenge to maintaining separation of duties (SOD)?

Answer: C

Explanation:
Separation of Duties (SOD) is intended to prevent errors or fraud by ensuring no single person (or entity) has control over all stages of a transaction. When AI automates financial approvals, the
"Lack of transparency in the algorithm" creates a challenge because the logic that grants approval is often opaque. If the algorithm functions as a "Black Box," it may consolidate decision- making power that was previously spread across multiple human roles, without sufficient visibility into how those decisions are made. This can lead to unauthorized or erroneous approvals going undetected. Effective AI governance must ensure that automated approvals are subject to transparent audit trails and secondary human reviews to uphold SOD principles.


NEW QUESTION # 61
Which of the following is the PRIMARY objective of AI governance?

Answer: B

Explanation:
The AAIATM Study Guide defines the primary objective of AI governance as establishing structure and accountability for AI initiatives. This includes clearly assigning responsibilities across development, deployment, risk management, and auditing roles to ensure that AI is used responsibly and transparently.
"AI governance establishes the policies, roles, and oversight structures that guide the ethical and secure deployment of AI. Clear accountability helps prevent unauthorized use and ensures strategic alignment."


NEW QUESTION # 62
Which of the following is the PRIMARY objective of AI governance?

Answer: B

Explanation:
The AAIA™ Study Guide defines the primary objective of AI governance as establishing structure and accountability for AI initiatives. This includes clearly assigning responsibilities across development, deployment, risk management, and auditing roles to ensure that AI is used responsibly and transparently.
"AI governance establishes the policies, roles, and oversight structures that guide the ethical and secure deployment of AI. Clear accountability helps prevent unauthorized use and ensures strategic alignment." Options A and C are essential components of governance but are not its core definition. Option D is a business outcome, not a governance goal. Thus, B is the most comprehensive and accurate objective.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Governance Objectives and Structures"


NEW QUESTION # 63
When auditing a research agency's use of generative AI models for analyzing scientific data, which of the following is MOST critical to evaluate in order to prevent hallucinatory results and ensure the accuracy of outputs?

Answer: B

Explanation:
Ensuring that input data is appropriate and relevant (option D) is the most critical factor in preventing hallucinations--where generative models produce fabricated or misleading outputs.
The AAIATM Study Guide notes, "Generative models are highly sensitive to input data; inaccurate, irrelevant, or inappropriate inputs increase the likelihood of nonsensical or incorrect outputs." While bias detection, data quality audits, and anonymization are important, ensuring the relevance and suitability of input data is foundational for reliable generative AI performance.


NEW QUESTION # 64
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