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ISACA AAISM Exam Syllabus Topics:

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

Section

Weight

Objectives

AI Risk Management

31%

- AI Threats and Vulnerabilities

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

- AI Risk Assessment

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

AI Governance and Program Management

31%

- AI Strategy and Policy

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

- Frameworks and Compliance

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

- AI Governance Foundations

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

AI Technologies and Controls

38%

- Data and Model Controls

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

- AI Assurance and Safety

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

- AI Security Architecture

1. Secure AI system design
2. Model lifecycle security

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
Within an incident handling process, which of the following would BEST help restore end-user trust in an AI system?

A. The AI model's outputs are validated by team members
B. The AI model prioritizes incidents based on business impact
C. Remediation of the AI system based on lessons learned
D. AI is used to monitor incident detection and alerts

Answer: C

Explanation:
AAISM highlights that post-incident remediation and demonstrating lessons learned is essential to restoring trust. Governance guidance specifies that stakeholders regain confidence only when organizations show clear corrective actions, transparency, and improvements to prevent recurrence.
Validating outputs (B) supports accuracy but is not trust-restoring. Monitoring (C) and prioritization (D) relate to operations, not trust rebuilding.
References: AAISM Study Guide - AI Governance; Incident Response and Trust Restoration.

NEW QUESTION # 67
Security and assurance requirements for AI systems should FIRST be embedded in the:

A. Model deployment phase
B. Model testing phase
C. Model training phase
D. Model design phase

Answer: D

Explanation:
AAISM directs organizations to embed security, safety, and compliance controls at design time ("secure-by- design" and "shift-left"), ensuring requirements for robustness, privacy, and governance are defined as non- functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.
References:* AI Security Management™ (AAISM) Body of Knowledge: Governance-Secure-by-Design; Policy-to-Control Traceability; Requirements Management* AAISM Study Guide: AI Program Lifecycle- Planning & Design Controls; Design-time Threat Modeling and Control Selection* AAISM Mapping to Standards: Design-phase Risk Identification and Requirements Engineering for AI

NEW QUESTION # 68
Which of the following approaches BEST helps to reduce model bias?

A. Decreasing the frequency of model updates
B. Ensuring diversity in training data sources
C. Utilizing a more complex model architecture
D. Increasing the number of labels per instance

Answer: B

Explanation:
AAISM frames bias risk primarily as a data problem. The most impactful mitigation is to ensure diversity and representativeness of training data sources, thereby reducing sampling bias and improving fairness across subpopulations.

NEW QUESTION # 69
Which of the following information is MOST important to include in a centralized AI inventory?

A. Foundation model and package registry
B. Training data sets
C. AI model use cases
D. Ownership and accountability of AI systems

Answer: D

Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.

NEW QUESTION # 70
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

A. Insufficient model validation and change control processes
B. Lack of sufficient computing resources for the AI system
C. Excessive reliance on external consultants for model design
D. Absence of metrics and dashboards for analysts

Answer: A

Explanation:
AAISM states that AI risk signals, thresholds, and model logic must be governed through strict validation and change control processes. Disabling key risk indicators without formal review or testing directly reflects a failure in:
* AI model validation
* Change management
* Governance oversight
This aligns precisely with option D.
Lack of dashboards (C) affects monitoring but does not explain disabled risk signals. Computing resources (A) would not cause intentional disabling. Reliance on consultants (B) is not connected to improper internal model changes.
References: AAISM Study Guide - AI Governance; Model Validation and Change Control Failures.

NEW QUESTION # 71
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