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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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
  • 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 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 (Q173-Q178):

NEW QUESTION # 173
What is the PRIMARY reason to establish a "kill switch" or manual override capability for an autonomous AI system?

Answer: A

Explanation:
A kill switch provides a critical safety control, allowing operators to immediately halt an AI system exhibiting harmful, erratic, or non-compliant behavior before further damage occurs.


NEW QUESTION # 174
An IS auditor uses an AI model to summarize worksheets, but several worksheets contained a " Hidden Cell " instructing the model to ignore control failures. Which solution BEST mitigates the risk?

Answer: B

Explanation:
This is a " Data-based Prompt Injection, " where the AI treats data as instructions. By " Allowing extraction only to predefined values and headers, " the auditor restricts the AI ' s " Context Window " to specific, known data fields. This prevents the AI from " reading " and executing malicious commands hidden in other cells.
High temperature (Option A) would only make the model more likely to follow erratic instructions.
Converting to PDF (Option D) is ineffective because modern AI models can still read the hidden text layers within a PDF.


NEW QUESTION # 175
In the context of an AI implementation, which of the following actions is MOST critical for an organization's change management program?

Answer: B

Explanation:
The AAIATM Study Guide emphasizes that AI implementations introduce dynamic and non- deterministic elements into systems, increasing the risk associated with changes. A comprehensive, AI-specific risk assessment is therefore the most critical component of a change management program to ensure that updates, retraining, or parameter adjustments do not introduce vulnerabilities or unintended consequences.
"Risk assessments tailored to AI are crucial because changes to models, training data, or infrastructure can affect performance, ethical compliance, or expose the system to new threats. A standard IT change review is often insufficient."


NEW QUESTION # 176
A generative AI system has a validation control in place to reject inappropriate questions by checking them against built-in ethical standards. Which of the following enables malicious actors to circumvent this control through prompt engineering?

Answer: D

Explanation:
Prompt engineering manipulation often involves disguising inappropriate questions in hypothetical or conditional formats, tricking the AI system into bypassing its filters. The AAIATM Study Guide highlights this as a common attack vector in generative AI misuse.
"Adversaries may reframe queries as academic or theoretical scenarios to exploit AI models' reasoning patterns, bypassing ethical constraints. Auditors should test for these circumvention techniques."


NEW QUESTION # 177
An IS auditor identified that an AI model based on historical data caused " data lag, " leading to poor real- time decision-making. Which of the following is the BEST mitigation strategy?

Answer: B

Explanation:
" Data lag " occurs when the model relies too heavily on old patterns that no longer reflect the current reality (e.g., pre-pandemic shopping habits). To mitigate this, " feature weight updating " is used to give more importance to the most recent data points while " decaying " the influence of older data. This allows the model to adapt more quickly to real-world shifts without requiring a full rebuild from scratch. While data diversity and volume are important for general performance, they do not specifically address the temporal " lag " issue like weight adjustment does.


NEW QUESTION # 178
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