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ISACA AAIA Exam Syllabus Topics:

Section	Objectives
AI Governance and Risk	- AI Governance Frameworks 1. AI risk management 2. Regulatory and compliance considerations 3. AI policies and governance structures - AI Security and Controls 1. Identity and access management 2. Threat and vulnerability management 3. Vendor and supply chain risk
AI Auditing Tools and Techniques	- AI Audit Planning 1. AI audit methodologies 2. Audit resource planning 3. Audit scoping and objectives - Audit Testing and Reporting 1. Testing and sampling methodologies 2. Evidence collection techniques 3. Audit reporting and follow-up
AI Operations	- AI System Lifecycle 1. Configuration and change management 2. Monitoring and maintenance 3. AI system development and deployment - AI Data Management 1. Training and testing data 2. Operational AI controls 3. Data quality and integrity

Section

Objectives

AI Governance and Risk

- AI Governance Frameworks

1. AI risk management
2. Regulatory and compliance considerations
3. AI policies and governance structures

- AI Security and Controls

1. Identity and access management
2. Threat and vulnerability management
3. Vendor and supply chain risk

AI Auditing Tools and Techniques

- AI Audit Planning

1. AI audit methodologies
2. Audit resource planning
3. Audit scoping and objectives

- Audit Testing and Reporting

1. Testing and sampling methodologies
2. Evidence collection techniques
3. Audit reporting and follow-up

AI Operations

- AI System Lifecycle

1. Configuration and change management
2. Monitoring and maintenance
3. AI system development and deployment

- AI Data Management

1. Training and testing data
2. Operational AI controls
3. Data quality and integrity

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ISACA Advanced in AI Audit Sample Questions (Q295-Q300):

NEW QUESTION # 295
Which of the following is the GREATEST risk associated with the use of AI coding tools by software developers?

A. Difficulty in training developers due to the complexity of AI tools
B. Excessive reliance on AI tools to accomplish routine development tasks
C. Increased likelihood of human biases in code
D. Introduction of security vulnerabilities by AI tools

Answer: D

Explanation:
AI-based coding assistants can inadvertently generate insecure code patterns, reuse vulnerable libraries, or bypass secure coding practices.
AAIA highlights security vulnerabilities as the primary risk because insecure code can lead to:
Data breaches
Injection attacks
Authentication bypasses
Supply chain compromise
Privilege escalation

NEW QUESTION # 296
A healthcare organization uses an AI model to analyze patient data and provide diagnostic recommendations.
Which of the following MOST effectively detects data drift related to the model's predictions?

A. Applying overrides to allow healthcare professionals to correct the AI model's recommendations
B. Conducting periodic model retraining to ensure alignment with updated patient data
C. Using adversarial testing to simulate scenarios that stress test the model's predictions
D. Comparing incoming patient data distributions with the training data set

Answer: D

Explanation:
Detecting data drift is critical in maintaining the reliability and accuracy of AI models, especially in dynamic environments like healthcare where patient populations and data characteristics can change over time.
According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide,data drift refers to changes in the input data's statistical properties compared to the data on which the model was originally trained.
If not detected, data drift can degrade model performance and lead to erroneous predictions.
Themost effective approach to detect data driftis tocontinuously compare the statistical distributions of incoming (production) data with those of the training dataset. This allows organizations to identify deviations in data patterns, which can be early indicators that the AI model's predictions may no longer be valid or optimal.
As stated in the AAIA™ Study Guide under "AI Model Monitoring and Maintenance":
"Monitoring input data for distributional changes compared to the model's training data is an essential step in identifying data drift. Statistical tests and visualizations can help auditors and AI operators detect when the underlying data characteristics have shifted, prompting further investigation or retraining needs." While options such as retraining the model (option C) or adversarial testing (option D) are valuable for ongoing performance and robustness,they do not inherently detect data drift-they respond to or stress-test existing issues. Applying overrides (option B) is a human-in-the-loop safeguard, not a method for drift detection.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Model Monitoring and Maintenance," Subsection: "Detection and Management of Data Drift"

NEW QUESTION # 297
An IS auditor is reviewing a dataset used by a university. Which of the following MOST likely indicates a risk that the model could not process all data and make necessary correlations?

A. Final Grade Percent field in object format
B. Grade Level field in float format
C. Student Number field in integer format
D. Having an undergraduate degree in Boolean format

Answer: A

Explanation:
In machine learning, "Data Typing" is foundational. If a numerical field (like Grade Percent) is stored as an "Object" (string/text), the AI model cannot perform mathematical operations on it, such as calculating correlations or averages. According to the AAIATM manual, this is a "Data Quality" failure that leads to "Feature Exclusion," where the model simply ignores the variable or treats each number as a unique text label, losing all quantitative meaning. The other formats (Integer, Float, Boolean) are appropriate for their respective data types.

NEW QUESTION # 298
Which of the following is the GREATEST risk associated with normalizing a data set before splitting it into training, testing, and validation sets?

A. The model affects data distribution.
B. The model requires external validation.
C. The developer uses tree-based models with the normalized data set.
D. The model gains indirect knowledge from data leakage.

Answer: D

Explanation:
Data normalization involves scaling data (e.g., ensuring all values are between 0 and 1). If you normalize the entire dataset before splitting it, the "Training Set" will be influenced by information from the "Testing Set" (such as the global maximum and minimum values). This is a form of "Data Leakage." According to the AAIATM manual, this "indirect knowledge" makes the model's test performance appear much better than it actually is, leading to a false sense of security. The correct procedure is to split the data first, then calculate normalization parameters using only the training data and apply those parameters to the test data.

NEW QUESTION # 299
Which of the following is MOST important to consider when auditing an organization's AI procedures?

A. Employee training on recognized AI best practices
B. Frequency of AI system updates to enhance security
C. Backup and recovery in the event of an AI data breach
D. AI data validation and filtration to prevent data poisoning

Answer: D

NEW QUESTION # 300
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