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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 Operations	- AI System Lifecycle 1. Monitoring and maintenance 2. AI system development and deployment 3. Configuration and change management - AI Data Management 1. Data quality and integrity 2. Operational AI controls 3. Training and testing data
AI Auditing Tools and Techniques	- Audit Testing and Reporting 1. Evidence collection techniques 2. Audit reporting and follow-up 3. Testing and sampling methodologies - AI Audit Planning 1. Audit resource planning 2. AI audit methodologies 3. Audit scoping and objectives

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 Operations

- AI System Lifecycle

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

- AI Data Management

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

AI Auditing Tools and Techniques

- Audit Testing and Reporting

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

- AI Audit Planning

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

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ISACA Advanced in AI Audit Sample Questions (Q270-Q275):

NEW QUESTION # 270
An organization is training a skin cancer recognition model. Photographs collected from which of the following sources would present the GREATEST risk associated with data integrity?

A. Cohort of dermatologists with signed patient consent forms
B. Open-source data augmentation files
C. Social media platform with images from all over the world
D. Research facility receiving grants for cancer research

Answer: C

Explanation:
Images from social media platforms (C) pose the greatest risk to data integrity because:
Images may be heavily filtered or edited
Lighting, scale, and resolution are inconsistent
Metadata is unreliable
Medical diagnosis labels (if any) are unverified
Context is unknown and may include misinformation
Sources cannot be validated or authenticated
AAIA stresses that medical AI systems require high-quality, clinically validated datasets with clear provenance, accuracy, and consent.

NEW QUESTION # 271
When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?

A. Difficulty of documenting AI algorithm processes
B. System output not being checked for inconsistencies
C. Inability to identify where an AI system is housed
D. Cascading failures of AI system outputs

Answer: B

Explanation:
Unchecked AI outputs pose a significant risk because incorrect or biased results may propagate into decision- making processes. The AAIA™ Study Guide stresses that validating AI outputs for consistency, accuracy, and reasonableness is a critical responsibility of both operators and auditors.
"Auditors must verify that appropriate output validation mechanisms are in place. Failure to check outputs increases the likelihood of undetected errors influencing downstream processes or decisions." While cascading failures (C) are a consequence of unchecked outputs, B is the root issue. A and D present logistical and documentation risks but not the highest immediate impact.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Audit Validation of AI Outputs"

NEW QUESTION # 272
The internal audit department of a large law firm plans to implement an AI solution to review and analyze contracts stored in its enterprise resource planning (ERP) system. Which of the following AI technologies is MOST suitable for this requirement?

A. Neural networks
B. Deep learning
C. Machine learning (ML)
D. Natural language processing (NLP)

Answer: D

Explanation:
Natural Language Processing (NLP) is the specific branch of AI focused on the interaction between computers and human language. For tasks involving the review, summarization, and extraction of insights from unstructured text documents like legal contracts, NLP is the most suitable technology. It allows the system to understand semantic meaning, identify clauses, and detect inconsistencies within a legal context.
While NLP often utilizes Deep Learning (Option A) and Neural Networks (Option B), those are the underlying " architectures, " whereas NLP is the specialized " application " required for document analysis.
ML (Option D) is a broad category that includes NLP but lacks the specificity required for linguistic processing.

NEW QUESTION # 273
An organization deploys an AI-based image recognition system that is vulnerable to evasion attacks. Which of the following approaches BEST helps to ensure the system mitigates these evasion attempts?

A. Static rule-based input filtering and validation
B. Adversarial training and regular bias reviews
C. Stringent model accuracy evaluation criteria
D. Implementation of model ensembles

Answer: B

Explanation:
Evasion attacks occur when an attacker modifies input data (such as adding subtle noise to an image) to trick a model into misclassification. The AAIATM manual identifies "Adversarial Training" as a primary defense, where the model is intentionally exposed to adversarial examples during the training phase to improve its robustness and resilience. This allows the model to learn the patterns associated with malicious inputs.

NEW QUESTION # 274
Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment?

A. Conducting a penetration test to identify vulnerabilities in the model
B. Reviewing the organization's AI software development life cycle documentation
C. Performing a business impact analysis (BIA) to assess the consequences of AI model failure
D. Assessing data lineage to verify the traceability of data sources

Answer: D

Explanation:
Assessing data lineage provides insight into the origin, flow, and transformation of data across its lifecycle, which is crucial for validating data governance. The AAIA™ Study Guide states that data lineage is essential to ensure the accuracy, consistency, and trustworthiness of data used in training AI models.
"Traceability of data sources is a core tenet of effective data governance. Data lineage validation ensures data quality, prevents unauthorized modifications, and maintains auditability." BIA (A) focuses on impact, not data quality. Reviewing SDLC (B) is broad and may not highlight data- specific risks. Penetration testing (C) addresses security, not governance. Therefore, D is the best method.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Quality, Integrity, and Governance Practices"

NEW QUESTION # 275
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