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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 (Q260-Q265):

NEW QUESTION # 260
When auditing AI systems, which data management practice BEST ensures compliance with privacy regulations?

Answer: A

Explanation:
Privacy regulations like GDPR and CCPA emphasize the principle of " Data Minimization " -collecting only the data that is strictly necessary for the intended purpose. In an AI context, organizations often feel tempted to collect as much data as possible to improve accuracy (Option A), but this increases privacy risk and legal exposure. According to ISACA, implementing data minimization is the most effective practice for ensuring regulatory compliance. It reduces the " attack surface " in the event of a breach and ensures that the organization is not holding onto sensitive PII that it cannot justify. Indefinite retention (Option D) is a direct violation of most modern privacy laws.


NEW QUESTION # 261
Which of the following is the PRIMARY benefit of using linear regression in machine learning (ML) when transparency and auditability are required?

Answer: A

Explanation:
Linear regression is a " transparent " or " white-box " model. Its primary benefit in an audit context is its interpretability; each coefficient directly indicates the influence of an input variable on the output. This allows auditors to easily verify the logic and ensure that no inappropriate or discriminatory variables are driving the results. While linear models are less prone to complex overfitting than deep learning, they are not immune to it. Their simplicity often results in lower predictive accuracy for complex data, but for auditability and compliance, the ability to explain the " why " behind a prediction is paramount.


NEW QUESTION # 262
Which of the following is the MOST important risk for an IS auditor to consider when reviewing the adoption of an AI system?

Answer: D


NEW QUESTION # 263
During a risk assessment for an AI system, data drift was identified as a key risk. Which of the following is the BEST course of action?

Answer: D

Explanation:
Data drift occurs when the statistical properties of input data change over time, causing the AI model to produce increasingly inaccurate or biased results.
The correct response is todocument the risk and implement continuous monitoring(A) because:
* Drift requiresongoing detection, not a one-time fix
* Retraining too early can reinforce issues (especially with the same dataset)
* Disabling the system (D) is overly disruptive unless drift results in unsafe decisions
* Continuing deployment without monitoring (C) increases risk of degraded performance AAIA emphasizesdrift monitoring dashboards, alerting mechanisms, and retraining triggersas essential controls for AI operations.
References:
AAIA Domain 2: AI Operations - Monitoring, Drift Detection, and Lifecycle Maintenance


NEW QUESTION # 264
Which of the following is MOST important for an IS auditor to consider when identifying AI risk in a know your customer (KYC) application within a banking organization?

Answer: C

Explanation:
In high-stakes financial applications like KYC, the primary concern is the potential business and regulatory impact of an AI error--such as false customer rejection or failure to detect fraudulent accounts. The AAIATM Study Guide emphasizes aligning AI risk assessments with business impact and regulatory exposure.
"In financial institutions, the most material risk of AI errors lies in operational disruption and regulatory fines. KYC models must be assessed for how errors can lead to compliance failures or reputational harm."


NEW QUESTION # 265
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