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ISACA AAISM Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: AI Technologies and Controls

38%

- Data and Model Controls

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

- AI Security Architecture

1. Model lifecycle security
2. Secure AI system design

- AI Assurance and Safety

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

Topic 2: AI Risk Management

31%

- AI Threats and Vulnerabilities

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

- AI Risk Assessment

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

Topic 3: AI Governance and Program Management

31%

- AI Strategy and Policy

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

- AI Governance Foundations

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

- Frameworks and Compliance

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q222-Q227):

NEW QUESTION # 222
To ensure ethical and responsible AI use, which AI usage policy metric is MOST important to monitor?

A. Frequency of policy reviews and updates
B. Number of policy violations
C. Number of AI projects reviewed for compliance
D. Frequency of policy consultations by employees

Answer: D

Explanation:
AAISM states the most meaningful policy performance metric is how often employees consult AI policies, which reflects:
- awareness
- practical adoption
- reliance on policy guidance
- safe decision-making behavior

NEW QUESTION # 223
A PRIMARY objective of responsibly providing AI services is to:

A. Build trust for decisions and predictions made by AI models
B. Enable AI models to operate autonomously
C. Improve the ability of AI models to learn from new data
D. Ensure the confidentiality and integrity of data processed by AI models

Answer: A

Explanation:
AAISM emphasizes that the primary objective of responsible AI is to establish and maintain trust in AI-driven decisions and predictions. Trust is achieved through transparency, accountability, fairness, and governance. While confidentiality and integrity are critical technical objectives, they are not the overarching purpose of responsible AI service provision. Autonomy and learning ability are features of AI, but without trust, adoption and compliance falter. The correct answer is that responsible AI services must focus on building trust in AI outcomes.

NEW QUESTION # 224
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?

A. Implementing restricted access to the model's internal parameters
B. Enhancing model robustness through adversarial training
C. Conducting periodic monitoring activities on the model's decisions
D. Applying differential privacy controls on training datasets

Answer: B

Explanation:
Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged. AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure: incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations). Monitoring (A) is detective, not preventive.
Restricting parameter access (C) protects confidentiality but does not mitigate input-space attacks.
Differential privacy (D) addresses training data leakage, not robustness to adversarial inputs.
References:AI Security Management (AAISM) Body of Knowledge: Adversarial ML-Evasion vs.
Poisoning; Robustness and Resilience Controls; Adversarial Training.AAISM Study Guide: Model Hardening Techniques; Evaluation of Robust Accuracy; Security Testing with Adversarial Examples.

NEW QUESTION # 225
During red-team testing of an AI system used to make lending decisions, which of the following techniques BEST simulates a data poisoning attack?

A. Stealing model weights from a deployed API
B. Corrupting training data sets to manipulate outcomes
C. Adding noise to output predictions
D. Inputting encrypted data into the model

Answer: B

Explanation:
AAISM defines data poisoning as the intentional manipulation of training data so that the learned model behaves incorrectly (e.g., skewed lending approvals/denials) while appearing valid. The correct simulation in red-team exercises is to corrupt or seed the training set with adversarial examples or mislabeled records to induce biased or erroneous decision boundaries. Encrypting inputs (A) is unrelated; output noise (B) describes perturbation of predictions, not training; model weight theft (C) is model extraction, not poisoning.
References: AI Security Management™ (AAISM) Body of Knowledge - Adversarial ML Threats; Data Poisoning and Training-Time Attacks. AAISM Study Guide - Red-Team Methods for AI; Poisoning vs.
Evasion vs. Model Extraction; Controls and Testing for Safety-Critical Decisions.

NEW QUESTION # 226
Which of the following BEST describes the role of risk documentation in an AI governance program?

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

Answer: B

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 # 227
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