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ISACA AAISM Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: AI Risk Management

31%

- AI Risk Assessment

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

- AI Threats and Vulnerabilities

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

Topic 2: AI Technologies and Controls

38%

- AI Assurance and Safety

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

- AI Security Architecture

1. Secure AI system design
2. Model lifecycle security

- Data and Model Controls

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

Topic 3: AI Governance and Program Management

31%

- AI Governance Foundations

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

- AI Strategy and Policy

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

- Frameworks and Compliance

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q375-Q380):

NEW QUESTION # 375
Which of the following methods provides the MOST effective protection against model inversion attacks?

A. Implementing regularization output
B. Using adversarial training
C. Reducing the model's complexity
D. Increasing the number of training iterations

Answer: A

Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
* A (adversarial training) targets perturbation robustness, not primary for inversion.
* B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
* D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.* AI Security Management™ Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.

NEW QUESTION # 376
An organization implements AI-driven behavioral analytics to identify insider threats and compromised credentials. Which of the following is the MOST significant security risk to be addressed by this deployment?

A. Manipulation by adversaries resulting in inaccurate threat identification
B. Complexities in threat identification due to rapid evolution of the local IT environment
C. Increased potential for password attacks and malicious prompt injection
D. Latency in responding to incidents due to AI alert processing

Answer: A

Explanation:
Adversaries can attempt to manipulate behavioral analytics models by injecting misleading patterns or adapting their behavior to evade detection, which can result in inaccurate threat identification and undermine the effectiveness of the system.

NEW QUESTION # 377
Which of the following should be included in an AI acceptable use policy?

A. AI monitoring requirements
B. Data collection and storage processes
C. AI training data requirements
D. Ethical and legal compliance standards

Answer: D

Explanation:
An AI acceptable use policy (AUP) sets the organizational expectations and boundaries for how AI systems may be used by employees and third parties. AAISM guidance places emphasis on ethical and legal compliance standards as core elements of an AUP to govern responsible behavior, prevent misuse, and align with regulatory and organizational principles. While data requirements, collection/storage processes, and monitoring may be covered in adjacent standards and procedures (e.g., data management policies, SOPs, and operational runbooks), the AUP's essential function is to codify permissible use anchored to ethics, legality, and organizational values.

NEW QUESTION # 378
A financial organization is concerned about the risk of prompt injection attacks on its customer service chatbot. Which of the following controls BEST addresses this concern?

A. Input validation
B. Continuous monitoring
C. Human-in-the-loop
D. Increasing model parameters

Answer: A

Explanation:
AAISM describes prompt injection as an attack where adversaries craft inputs that manipulate model behavior or override system instructions. The recommended control pattern is to implement robust input validation and constraint mechanisms that sanitize and structure user inputs before they are processed by the model. The guidance includes techniques such as template-based prompts, restricted instruction sets, and validation rules to filter malicious or out- of-scope content.

NEW QUESTION # 379
An AI fraud detection system in a bank uses transaction data that includes customer names and account numbers. Which of the following BEST helps the bank comply with privacy regulations and data classification policies?

A. Conducting awareness training for AI developers
B. Security training to avoid leakage of personal data
C. Performing periodic audits of the AI model for compliance
D. Masking or tokenizing customer-related data in use

Answer: D

Explanation:
Masking or tokenizing customer names and account numbers directly protects sensitive and regulated data while allowing the AI system to continue processing transactions. This is the most effective control for supporting compliance with privacy regulations and data classification requirements.

NEW QUESTION # 380
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