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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Governance and Program Management	31%	- Frameworks and Compliance 1. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001) 2. Privacy and ethics considerations 3. Regulatory requirements - AI Governance Foundations 1. Roles and responsibilities 2. Organizational structure and governance 3. Stakeholder considerations - AI Strategy and Policy 1. Policies and procedures 2. Responsible and acceptable use 3. AI strategy development
Topic 2: AI Risk Management	31%	- AI Risk Assessment 1. Risk identification and analysis 2. Risk thresholds and treatment - AI Threats and Vulnerabilities 1. Adversarial AI and ML attacks 2. Data poisoning and model manipulation 3. Supply chain and vendor risks
Topic 3: AI Technologies and Controls	38%	- Data and Model Controls 1. Data protection and governance 2. Monitoring and runtime controls 3. Privacy-preserving techniques - AI Assurance and Safety 1. Bias mitigation 2. Model validation and testing 3. Ethical AI controls - AI Security Architecture 1. Model lifecycle security 2. Secure AI system design

Section

Weight

Objectives

Topic 1: AI Governance and Program Management

31%

- Frameworks and Compliance

1. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001)
2. Privacy and ethics considerations
3. Regulatory requirements

- AI Governance Foundations

1. Roles and responsibilities
2. Organizational structure and governance
3. Stakeholder considerations

- AI Strategy and Policy

1. Policies and procedures
2. Responsible and acceptable use
3. AI strategy development

Topic 2: AI Risk Management

31%

- AI Risk Assessment

1. Risk identification and analysis
2. Risk thresholds and treatment

- AI Threats and Vulnerabilities

1. Adversarial AI and ML attacks
2. Data poisoning and model manipulation
3. Supply chain and vendor risks

Topic 3: AI Technologies and Controls

38%

- Data and Model Controls

1. Data protection and governance
2. Monitoring and runtime controls
3. Privacy-preserving techniques

- AI Assurance and Safety

1. Bias mitigation
2. Model validation and testing
3. Ethical AI controls

- AI Security Architecture

1. Model lifecycle security
2. Secure AI system design

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q236-Q241):

NEW QUESTION # 236
An organization is implementing a large language model (LLM) to answer questions for both employees and customers. The CIO wants the LLM to provide employees with additional internal data that should not be shared with customers. Which of the following is the MOST secure approach?

A. Allow list employee access
B. Retrieval-augmented generation (RAG) with access control lists (ACLs)
C. Model fine tuning with increased use of function calls
D. Prompt sanitization

Answer: B

Explanation:
Retrieval-augmented generation (RAG) combined with access control lists (ACLs) ensures that the LLM retrieves and exposes only the data each user is authorized to access. This approach provides granular, role-based protection of internal information while supporting different data visibility requirements for employees and customers.

NEW QUESTION # 237
An internet service provider (ISP) uses a chatbot based on a public model to help resolve connectivity issues. Customers report that the chatbot's instructions lack details about the ISP's proprietary equipment. Which of the following is the BEST way to improve the user experience?

A. Input filtering
B. Retrieval-augmented generation (RAG)
C. Use of additional public models
D. Output sanitization

Answer: B

Explanation:
Retrieval-augmented generation integrates the chatbot with the organization's proprietary knowledge sources, enabling it to provide accurate and detailed responses specific to the ISP's equipment while maintaining the strengths of the base model.

NEW QUESTION # 238
Employees are regularly using open-source generative AI without guidance. What should be the CISO's GREATEST concern?

A. Data leakage
B. Policy violations
C. Lack of monitoring
D. Model hallucinations

Answer: A

Explanation:
AAISM emphasizes that the primary and most severe risk of uncontrolled generative AI usage is data leakage, including:
- confidential data
- regulated personal data
- intellectual property
While hallucinations (A) are harmful, they do not cause direct data loss. Lack of monitoring (C) is a secondary risk. Policy violations (D) occur because of lack of controls but do not outweigh the impact of leaked sensitive data.

NEW QUESTION # 239
Which of the following AI data management techniques involves creating validation and test data?

A. Splitting
B. Annotating
C. Learning
D. Training

Answer: A

Explanation:
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.

NEW QUESTION # 240
Which testing technique is BEST for determining how an AI model makes decisions?

A. White box
B. Black box
C. Blue team
D. Red team

Answer: A

Explanation:
AAISM indicates that white-box testing allows evaluators full visibility into:
* internal logic
* weights
* decision pathways
* model architecture
This makes it ideal for understanding how decisions are made.
Black box (B) provides no internal visibility. Red/blue team tests (A, D) focus on security, not decision mechanics.
References: AAISM Study Guide - AI Testing; Explainability Through White-Box Analysis.

NEW QUESTION # 241
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