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| Section | Objectives |
|---|---|
| AI Operations | - AI System Lifecycle
|
| AI Auditing Tools and Techniques | - AI Audit Planning
|
| AI Governance and Risk | - AI Security and Controls
|
>> Valid Real ISACA AAIA Exam <<
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NEW QUESTION # 181
An insurance company uses an AI model to set premium rates. To align with AI-related policies on fairness, which of the following is the FIRST course of action?
Answer: B
Explanation:
AAIA guidance states thatfairness evaluations begin with the training data, because bias is most commonly introduced through data selection, sampling imbalances, labeling inconsistencies, or historical discrimination embedded in source data.
Thus, thefirstcourse of action is to review training data (option B) to identify:
* Skewed demographic distributions
* Missing or underrepresented populations
* Inappropriate use of sensitive attributes
* Incorrect labels or improperly encoded variables
* Historical decisions that may propagate discrimination
Only after identifying the existence and nature of bias can an organization move on to remediation steps such as retraining (C) or comparing alternate model behavior (A).
Allowing customers to contest premiums (D) is a post-decision remedy, not a fairness evaluation step.
AAIA emphasizes addressing bias at theroot-the training data-before adjusting the model or outputs.
References:
AAIA Domain 5: Fairness, Bias Identification, Ethical AI Principles
AAIA Domain 2: Data Governance and Input Evaluations
NEW QUESTION # 182
An IS auditor is evaluating a cybersecurity system that uses agentic AI for autonomous threat detection and incident response. Which of the following is MOST important for the auditor to consider?
Answer: B
Explanation:
" Agentic AI " goes beyond simple detection; it can take actions (like shutting down a server) autonomously.
The greatest risk is that the AI might misinterpret a legitimate but unusual business activity as a threat and trigger a " kill switch, " causing massive, unplanned downtime. This is a significant operational and availability risk. Auditors must ensure there are " guardrails " or a " human-in-the-loop " for high-impact actions. While reduced intervention (Option B) is a benefit, it is also the source of the risk that the auditor must mitigate through governance and control evaluation.
NEW QUESTION # 183
An organization is using a large language model (LLM) to assist in evaluating loan applications, but the training data used is known to be incomplete. Which of the following is the GREATEST associated risk?
Answer: C
Explanation:
Incomplete training data often leads to underrepresentation of certain applicant types, products, or scenarios. In credit and lending, this typically translates into systematic bias : some groups are evaluated on richer historical patterns, while others are evaluated on sparse or unrepresentative information. The greatest associated risk is therefore unfair loan decisions (A), which can manifest as unjustified rejections, inappropriate pricing, or inconsistent risk assessments.
While delays (B), reduced satisfaction (C), or increased manual work (D) may occur, they are secondary operational issues. AAIA highlights that for financial services, the central risks include fairness, discrimination, regulatory compliance, and reputational impact . Incomplete data directly undermines fairness and can violate lending regulations and internal risk appetite.
References:
ISACA, AAIA Exam Content Outline - Domain 1: AI Governance and Risk (risk categories, including fairness and discriminatory outcomes).
ISACA AI ethics content on data completeness and representativeness in decisioning systems.
NEW QUESTION # 184
When auditing AI systems, which data management practice BEST ensures compliance with privacy regulations?
Answer: B
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 # 185
Which of the following is the GREATEST challenge facing IS auditors evaluating the explainability of generative AI models?
Answer: B
Explanation:
The greatest challenge for IS auditors in evaluating the explainability of generative AI models is the changing nature of algorithms as AI continues to learn (option D). Generative AI models, especially those using advanced techniques like deep learning and reinforcement learning, often employ continuous or dynamic learning, which results in models that evolve over time. This adaptability can significantly hinder explainability because the logic, parameters, or decision pathways may shift with ongoing retraining or real- time learning.
The ISACA Advanced in AI Audit™ (AAIA™) Study Guide stresses that: "Continually learning AI systems present unique audit challenges, as their internal representations and reasoning can change after deployment, making it difficult to fully capture and explain the rationale for outputs at any given point." Other options, such as bias in input data or computational performance, are significant but do not pose as fundamental a challenge to explainability as a model whose internal workings can dynamically change.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Explainability and Dynamic Models"
NEW QUESTION # 186
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