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

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

Topic 2: AI Technologies and Controls

38%

- AI Assurance and Safety

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

- AI Security Architecture

1. Model lifecycle security
2. Secure AI system design

- 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%

- Frameworks and Compliance

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

- AI Strategy and Policy

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

- AI Governance Foundations

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q205-Q210):

NEW QUESTION # 205
Which of the following reviews MUST be conducted as part of an AI impact assessment?

A. Security control self-assessment (CSA)
B. Evaluation of model reproducibility
C. Identification of environmental and societal consequences
D. Testing, evaluation, validation, and verification

Answer: C

Explanation:
The AAISM material frames an AI impact assessment as broader than a technical model review. It emphasizes that governance of AI includes assessing societal, environmental, and human-rights impacts in addition to organizational risk. The official guidance notes that high-level impact assessments must explicitly consider "potential consequences for individuals, communities, and the environment" as part of responsible AI governance. Activities such as testing/validation (A) and reproducibility (B) are critical in the development lifecycle but are not, by themselves, sufficient to constitute an impact assessment. A CSA (C) is focused on security controls, which is narrower than overall impact. The mandatory element that differentiates an AI impact assessment is the assessment of environmental and societal consequences, making option D the correct answer in alignment with governance expectations.
References: AI Security Management™ (AAISM) Study Guide - AI Governance and Impact Assessments; Responsible and Trustworthy AI Section.

NEW QUESTION # 206
Which of the following BEST ensures the integrity of data sets used to train AI models?

A. Appropriate storage of data sets according to documented classification processes
B. Collection and retention of only necessary data sets
C. Tracking and verification of data sets via cryptographic controls
D. Clear documentation of data sources, types used, and processing steps

Answer: C

Explanation:
AAISM defines cryptographic tracking and verification as the best control for ensuring the integrity of training data. By applying hashing and verification methods, organizations can confirm that datasets remain unaltered and authentic throughout collection, storage, and processing.
Collecting only necessary data, proper storage, or clear documentation all support governance and compliance, but they do not guarantee that the data has not been tampered with. Integrity is specifically ensured by cryptographic verification techniques.

NEW QUESTION # 207
Which defense is MOST effective against cyberattacks that alter input data to avoid detection?

A. Restricting access to internal model parameters
B. Conducting periodic monitoring of decisions
C. Applying differential privacy to training data
D. Enhancing model robustness through adversarial training

Answer: D

Explanation:
AAISM lists adversarial training as the strongest method to harden models against input manipulation attacks.
By exposing models to adversarial examples during training, the system learns to resist evasion techniques.
Access restriction (B) protects confidentiality, not detection evasion. Monitoring (C) is reactive, not preventive. Differential privacy (D) protects individual data, not adversarial inputs.
References: AAISM Study Guide - AI Evasion Attacks; Adversarial Training Mitigation.

NEW QUESTION # 208
An information security manager has implemented key performance indicators (KPIs) and key risk indicators (KRIs) to oversee the security posture of an AI-based fraud detection system.
Which of the following would be of GREATEST concern?

A. Indicators primarily focus on quantitative fraud and threat data.
B. Indicators fail to capture adversarial techniques targeting AI components.
C. Indicators do not include comparisons against industry competitors.
D. Indicators also assess network traffic unrelated to AI functions.

Answer: B

Explanation:
If KPIs and KRIs do not account for adversarial techniques targeting AI components, critical AI- specific threats such as evasion, poisoning, or model manipulation may go undetected, leaving significant gaps in the security posture.

NEW QUESTION # 209
Which of the following BEST describes the role of risk documentation in an AI governance program?

A. Outlining the acceptable levels of risk for AI-related initiatives
B. Offering detailed analyses of technical risk and vulnerabilities
C. Providing a record of past AI-related incidents for audits
D. Demonstrating governance, risk, and compliance (GRC) for external stakeholders

Answer: A

Explanation:
In AAISM governance guidance, risk documentation is described as the structured record that defines the organization's risk appetite and tolerance levels for AI initiatives. By outlining acceptable levels of risk, documentation ensures decision-makers can approve, monitor, and adjust AI projects within defined boundaries. While it may also serve audit functions, technical analysis, or communication to stakeholders, its primary role is to formalize risk acceptance thresholds and integrate them into governance and decision- making. This aligns directly with the governance requirement to align AI adoption with organizational risk appetite.
References:
AAISM Study Guide - AI Governance and Program Management (Risk Documentation and Appetite) ISACA AI Security Management - Governance, Risk and Compliance Integration

NEW QUESTION # 210
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