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ISACA AAIA Exam Syllabus Topics:

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

Section

Objectives

Topic 1: AI Governance and Risk

- AI Governance Frameworks

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

- AI Security and Controls

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

Topic 2: AI Auditing Tools and Techniques

- AI Audit Planning

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

- Audit Testing and Reporting

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

Topic 3: AI Operations

- AI System Lifecycle

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

- AI Data Management

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

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ISACA Advanced in AI Audit Sample Questions (Q259-Q264):

NEW QUESTION # 259
A bank is deploying an AI model trained on customer transactions in a multi-cloud environment. Which of the following BEST supports regulatory compliance regarding data security and privacy?

A. Data governance framework
B. Data residency and sovereignty controls
C. Data protection impact assessment (DPIA)
D. Privacy by design

Answer: B

Explanation:
In a multi-cloud environment, data often traverses various geographical regions. For financial institutions, " Data residency and sovereignty " are the most critical compliance factors, ensuring that data is stored and processed within the jurisdictions required by law (e.g., GDPR or local banking acts). Failure to control where data resides can lead to severe legal penalties. While " Privacy by Design " and " DPIA " are essential methodologies, they do not specifically address the physical and legal location of data across cloud providers.
Sovereignty controls ensure the bank maintains control over its data assets and complies with specific cross- border data transfer regulations.

NEW QUESTION # 260
An IS auditor is looking to expedite reporting for an audit with complex issues. Which of the following would be the MOST effective way for the auditor to use generative AI?

A. Developing a draft of an executive summary based on detailed findings and audit scope
B. Developing action items discussed in closing meetings for management action plans
C. Revising audit background and scope information based on new information from management
D. Revising audit conclusions with precise verbiage to describe the audit observations

Answer: A

Explanation:
The AAIA™ Study Guide highlights that generative AI tools can significantly enhance audit productivity, especially in content generation tasks. Drafting executive summaries-condensing complex findings into clear, concise formats-is a high-impact use case that benefits from generative AI's natural language generation capabilities.
"Generative AI can be leveraged by auditors to automate reporting sections, such as executive summaries, ensuring consistency and saving time without compromising content clarity." While A, C, and D are valid uses, B delivers the most value by expediting a time-intensive and high-level reporting task in complex audits.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Generative AI Applications in Audit Reporting"

NEW QUESTION # 261
Which of the following is the BEST way to support the development and design of high-risk AI systems?

A. Conduct regular training sessions for users on data privacy.
B. Ensure the availability of trustworthy data sets.
C. Regularly back up the AI system's data to a secure, offsite location.
D. Implement multi-factor authentication (MFA) for all users accessing the AI system.

Answer: B

NEW QUESTION # 262
The PRIMARY benefit of using AI in audit fieldwork is that it:

A. Helps auditors to cover testing processes of all risk levels.
B. Can scan large datasets to identify trends and anomalies.
C. Automates documentation of workpapers and artifacts.
D. Saves time for the engagement manager in reviewing the audit work.

Answer: B

Explanation:
According to the AAIATM manual, the transformative power of AI in audit fieldwork lies in its ability to process 100% of a data population at scale. While traditional audit methods rely on manual sampling, which may miss infrequent or complex outliers, AI-driven tools can efficiently scan massive, multi-dimensional datasets to pinpoint anomalies and emerging trends that warrant further investigation. This increases the depth of the audit and allows for a more rigorous risk- based approach.

NEW QUESTION # 263
Which of the following is the GREATEST risk when a generative AI tool used for threat detection produces inaccurate or misleading information?

A. Potential threats to organizational systems may be overlooked.
B. AI-related key risk indicator (KRI) values may become less reliable.
C. Prompt injection attacks may become more likely to succeed.
D. The model may exaggerate the severity of threats and vulnerabilities.

Answer: A

Explanation:
In the context of cybersecurity and threat detection, the "accuracy" of AI outputs is a matter of organizational safety. The greatest risk of inaccurate information (such as false negatives) is that
"Potential threats may be overlooked," leading to undetected breaches or system compromises.
This occurs when the model fails to flag a genuine anomaly as suspicious.

NEW QUESTION # 264
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