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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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AAIA Actual Questions | AAIA Actual Braindumps

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ISACA Advanced in AI Audit Sample Questions (Q107-Q112):

NEW QUESTION # 107
An organization is using information gathered from customer accounts to train its AI chatbot. Which of the following is the GREATEST risk associated with this practice?

Answer: A

Explanation:
The use of customer data in AI training presents a significant privacy risk, especially when the data is not properly anonymized or when consent has not been explicitly obtained. The AAIA™ Study Guide classifies the unauthorized or inadvertent disclosure of personally identifiable information (PII) as the most severe risk in such contexts.
"Training AI models on customer data without proper safeguards can result in the unintentional exposure of personal or sensitive information, leading to data breaches and compliance violations." While bias (B) and hallucinations (D) are important operational concerns, they do not pose the same regulatory and ethical severity as PII exposure. Therefore, A is the most critical risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Privacy Risks and Data Minimization"


NEW QUESTION # 108
Which of the following is the MOST important task when gathering data during the AI system development process?

Answer: A


NEW QUESTION # 109
An IS auditor finds that an AI model's outputs are not being reviewed. Which of the following would BEST address this risk?

Answer: D

Explanation:
When AI outputs are not being reviewed, the primary concern is that incorrect, biased, or non- compliant decisions may go undetected. The best way to address this risk is to implement a validation process for AI decisions (B), which may include human-in-the-loop checks, sampling- based reviews, escalation criteria, and formal approval workflows. AAIA emphasizes the importance of supervision and validation of AI outputs, particularly where decisions have financial, legal, or safety implications.


NEW QUESTION # 110
During a walk-through, an IS auditor observes an AI engineer entering a prompt that manipulates the AI model's behavior. Which of the following is the BEST control to prevent this?

Answer: B

Explanation:
The most direct and effective control for preventing prompt-based manipulation is toenforce a structured input/output template(option A). AAIA highlights prompt management as a key emerging control area because unstructured prompts can lead to:
* Prompt injection
* Model manipulation
* Circumvention of rules
* Unauthorized access to sensitive outputs
* Safety violations
Templates constrain user input to predefined formats, reducing opportunities to embed hidden instructions or modify model behavior.
Adversarial training (B) strengthens robustness but doesnotprevent users from inserting manipulative prompts.
Encryption (C) protects data, not prompt integrity.
Immediate retraining (D) addresses performance, not misuse.
Templates ensure consistent, secure, and auditable prompt structure.
References:
AAIA Domain 1: AI Governance, Prompt Controls
AAIA Domain 2: Security Controls for Generative AI


NEW QUESTION # 111
An organization implements a neural network-based AI system for credit scoring that processes extensive input data from multiple sources. Which of the following represents the GREATEST risk if input validation and anomaly detection controls are inadequate?

Answer: C

Explanation:
In neural network-based credit scoring, the model ' s complexity often obscures the relationship between specific inputs and final outcomes. If input validation and anomaly detection are weak, " biased inputs " or " noise " from various sources can seep into the training and inference pipelines. According to the ISACA AAIA™ framework, these biased inputs can cause the model to learn discriminatory patterns (e.g., correlations between ethnicity and creditworthiness) that are hidden within large datasets. This leads to unethical and potentially illegal credit decisions. While overfitting (Option B) and costs (Option C) are technical concerns, the systemic risk of automated discrimination via unchecked inputs poses the most significant threat to the organization ' s legal standing and reputation.


NEW QUESTION # 112
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