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

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

Topic 2: AI Technologies and Controls

38%

- AI Security Architecture

1. Model lifecycle security
2. Secure AI system design

- Data and Model Controls

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

- AI Assurance and Safety

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

Topic 3: AI Governance and Program Management

31%

- Frameworks and Compliance

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

- AI Strategy and Policy

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

- AI Governance Foundations

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

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ISACA AAISM Test Pattern, Valid AAISM Test Notes

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
When documenting information about machine learning (ML) models, which of the following artifacts BEST helps enhance stakeholder trust?

A. Hyperparameters
B. Data quality controls
C. Model prototyping
D. Model card

Answer: D

Explanation:
Themodel cardis a governance artifact that communicatesintended use, performance characteristics, limitations, fairness considerations, and ethical notesof an ML model.
AAISM governance materials highlight thatstakeholder trustcomes from transparency and explainability.
While hyperparameters and data quality controls are technical details, they lack stakeholder-facing clarity.
Model prototyping is part of development but not a governance record.
Model cards are explicitly recommended to:
* Provideexplainabilityand context for decision-making.
* Demonstrate governance, transparency, and compliance.
* Enable stakeholders (regulators, auditors, business leaders) to trust the system.
Therefore, themodel cardis the artifact that best enhances trust.

NEW QUESTION # 83
The PRIMARY goal of data poisoning attacks is to:

A. undermine the integrity of the AI system's outputs
B. compromise the confidentiality of model input data
C. manipulate the behavior of the model during development
D. compromise the confidentiality of output data from the model

Answer: A

Explanation:
AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine- tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production.

NEW QUESTION # 84
A large corporation has received an influx of sophisticated credential-phishing emails and wants to leverage an AI solution to detect and quarantine these messages before they reach employees. Which of the following blue-team AI features is BEST suited to this task?

A. Retrieval-augmented generation (RAG)
B. Natural language processing (NLP)
C. Large language model (LLM)
D. Natural language generation (NLG)

Answer: B

Explanation:
For pre-delivery phishing detection and classification, the most appropriate capability is NLP- tokenization, feature extraction, semantic similarity, and supervised classifiers (e.g., transformer-based classifiers) tuned on phishing corpora and indicators. NLP models score messages and drive automated quarantine policies. An LLM (Option A) is a model type, not a specific blue-team feature; NLG (Option C) is for generation, not detection; RAG (Option D) augments responses with retrieved knowledge but does not by itself optimize classification and quarantine of phishing emails.
References:
AAISM Body of Knowledge: Defensive AI Use Cases; Text Classification Pipelines for Security Operations; Email Security and AI-Driven Triage.
AAISM Study Guide: NLP for Threat Detection; Model Evaluation for Precision/Recall in Security Classifiers; SOC Integration and Automated Containment.

NEW QUESTION # 85
Which of the following processes involves tagging and labeling raw datasets to create training data sets?

A. Categorization
B. Classification
C. Annotation
D. Transformation

Answer: C

Explanation:
Annotation is the process of tagging, labeling, or marking raw data so it can be used as structured training data for machine learning and AI models. This enables the model to learn patterns, categories, or relationships from the labeled examples.

NEW QUESTION # 86
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?

A. Initiate discussions between the organization's and the vendor's legal teams
B. Ensure vendors disclose how the application uses the organization's data
C. Assess the vendor's publicly available AI usage policy
D. Mandate an AI security audit by an external auditor before procurement

Answer: B

Explanation:
AAISM's approach to third-party and vendor risk for AI systems stresses data usage transparency as a primary control. The guidance explains that organizations must obtain clear documentation on "what data is collected, how it is processed, stored, retained, and whether it is reused for training or shared with other parties." Option C directly addresses this by requiring the vendor to disclose how the application uses organizational data, enabling appropriate risk assessment, contractual controls, and technical safeguards. An external audit (A) can be useful but may be costly and not always feasible pre-procurement. Legal discussions (B) are important but ineffective without clarity on data flows. Publicly available policies (D) are often high- level and marketing-oriented, lacking the specificity required for proper risk evaluation. Therefore, obtaining explicit data usage disclosures from the vendor is the most effective starting point.
References: AI Security Management™ (AAISM) Study Guide - Third-Party AI Risk and Data Sharing; Vendor Governance Requirements.

NEW QUESTION # 87
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