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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
AI Technologies and Controls	38%	- AI Security Architecture 1. Secure AI system design 2. Model lifecycle security - AI Assurance and Safety 1. Ethical AI controls 2. Bias mitigation 3. Model validation and testing - Data and Model Controls 1. Privacy-preserving techniques 2. Monitoring and runtime controls 3. Data protection and governance
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. AI strategy development 2. Policies and procedures 3. Responsible and acceptable use - AI Governance Foundations 1. Stakeholder considerations 2. Organizational structure and governance 3. Roles and responsibilities - Frameworks and Compliance 1. Regulatory requirements 2. Privacy and ethics considerations 3. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001)

Section

Weight

Objectives

AI Technologies and Controls

38%

- AI Security Architecture

1. Secure AI system design
2. Model lifecycle security

- AI Assurance and Safety

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

- Data and Model Controls

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

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

- AI Governance Foundations

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

- Frameworks and Compliance

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q320-Q325):

NEW QUESTION # 320
An organization's CIO provided the AI steering committee with a list of AI technologies in use and tasked them with categorizing the technologies by risk. Which of the following should the committee do FIRST?

A. Begin grouping similar AI products and solutions together
B. Identify vulnerabilities related to the technologies in use
C. Ensure the AI technologies are included in the asset inventory
D. Assess risk levels based on risk appetite and regulatory requirements

Answer: C

Explanation:
AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance.
Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.

NEW QUESTION # 321
To ensure the ethical and responsible use of AI, which of the following AI usage policy metrics is MOST important for an organization to monitor?

A. Frequency of policy reviews and updates
B. Frequency of policy consultations by employees
C. Number of reported policy violations
D. Number of AI projects that have undergone policy compliance review

Answer: D

Explanation:
AAISM emphasizes governance effectiveness metrics tied to real lifecycle checkpoints. The count (and percentage) of AI projects that completed policy compliance review before deployment is a leading indicator of policy enforcement and assurance. It directly reflects whether responsible-AI requirements (risk assessment, impact assessment, data/privacy checks, security controls) are embedded in practice. Consult frequency (A) and review cadence (D) are activity metrics, not outcomes. Reported violations (B) are lagging indicators and can be deceptive (low numbers may indicate under-reporting).
References:* AI Security Management (AAISM) Body of Knowledge: Program KPIs-policy adoption, stage-gate compliance, audit readiness* AAISM Study Guide: Governance metrics for Responsible AI- coverage of reviews, pass/fail rates, exceptions handling

NEW QUESTION # 322
An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data.
Which of the following is the MOST effective data cleaning technique to improve model performance?

A. Applying statistical methods to address missing data and reduce bias
B. Deleting outlier data points to prevent unusual values impacting the model
C. Increasing the frequency of model retraining with the existing data set
D. Tuning model hyperparameters to increase performance and accuracy

Answer: A

Explanation:
The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction.
Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.
References:
AAISM Study Guide - AI Risk Management (Data Integrity and Quality Controls) ISACA AI Governance Guidance - Data Preparation and Bias Mitigation

NEW QUESTION # 323
Which strategy BEST ensures generative AI tools do not expose company data?

A. Implementing a solution prohibiting input of sensitive data
B. Ensuring AI tools comply with local regulations
C. Testing AI tools before implementation
D. Conducting an independent AI data audit

Answer: A

Explanation:
AAISM identifies the strongest immediate control for preventing data leakage into generative AI systems as technically restricting or blocking user entry of sensitive data.

NEW QUESTION # 324
Which of the following is the BEST reason to immediately disable an AI system?

A. Excessive model drift
B. Slow model performance
C. Overly detailed model outputs
D. Insufficient model training

Answer: A

Explanation:
According to AAISM lifecycle management guidance, the best justification for disabling an AI system immediately is the detection of excessive model drift. Drift results in outputs that are no longer reliable, accurate, or aligned with intended purpose, creating significant risks. Performance slowness and overly detailed outputs are operational inefficiencies but not critical shutdown triggers. Insufficient training should be addressed before deployment rather than after. The trigger for immediate deactivation in production is excessive drift compromising reliability.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Model Drift Management) AI Security Management Study Guide - Disabling AI Systems

NEW QUESTION # 325
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