AAISM Online Exam | Exam AAISM Study Guide

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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Technologies and Controls	38%	- Data and Model Controls 1. Monitoring and runtime controls 2. Data protection and governance 3. Privacy-preserving techniques - AI Security Architecture 1. Secure AI system design 2. Model lifecycle security - AI Assurance and Safety 1. Bias mitigation 2. Ethical AI controls 3. Model validation and testing
Topic 2: 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 Governance Foundations 1. Organizational structure and governance 2. Roles and responsibilities 3. Stakeholder considerations - AI Strategy and Policy 1. Policies and procedures 2. Responsible and acceptable use 3. AI strategy development
Topic 3: 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 thresholds and treatment 2. Risk identification and analysis

Section

Weight

Objectives

Topic 1: AI Technologies and Controls

38%

- Data and Model Controls

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

- AI Security Architecture

1. Secure AI system design
2. Model lifecycle security

- AI Assurance and Safety

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

Topic 2: 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 Governance Foundations

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

- AI Strategy and Policy

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

Topic 3: 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 thresholds and treatment
2. Risk identification and analysis

>> AAISM Online Exam <<

Exam ISACA AAISM Study Guide | AAISM Test Dumps.zip

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?

A. Model owners
B. End users
C. System architects
D. Internal auditors

Answer: A

Explanation:
AAISM clarifies that model owners hold responsibility for ensuring corrective actions are implemented after AI audits. They are accountable for:
- model behavior
- compliance gaps
- security improvements
- governance alignment

NEW QUESTION # 59
An organization is commissioning a third-party AI system using sensitive data. Which metric is MOST important to consider?

A. Accuracy thresholds
B. Model response time
C. Service availability
D. Accessibility rating

Answer: A

Explanation:
AAISM specifies that when AI systems process sensitive or high-impact data (e.g., financial, identity, health), accuracy thresholds become critical risk indicators. Inaccurate predictions can cause harm, regulatory non-compliance, discrimination, or financial loss.

NEW QUESTION # 60
An organization has discovered that employees have started regularly utilizing open-source generative AI without formal guidance. Which of the following should be the CISO's GREATEST concern?

A. Lack of monitoring
B. Data leakage
C. Model hallucinations
D. Policy violations

Answer: B

Explanation:
The greatest immediate risk from unsanctioned use of public or open-source generative AI tools is data leakage-employees may paste confidential or regulated information into third-party systems, resulting in loss of confidentiality, regulatory exposure, and loss of intellectual property. AAISM emphasizes that when AI use occurs outside approved channels, the top control priority is preventing exfiltration of sensitive data via prompts, attachments, and context sharing. Monitoring and policy are necessary enablers, but leakage is the highest-impact failure mode in the short term; hallucinations primarily affect accuracy, not confidentiality.
References:* AI Security Management™ (AAISM) Body of Knowledge: Generative AI governance; human- in-the-loop risks; data loss and exfiltration vectors in prompts; sanctioned vs. unsanctioned AI usage.* AI Security Management™ Study Guide: Immediate risk triage for shadow AI; DLP and input-control safeguards; confidentiality-first posture for generative AI adoption.

NEW QUESTION # 61
A global organization has experienced multiple incidents of staff copying confidential data into public chatbots and acting on the model outputs. Which of the following is MOST important to reduce short-term risk when launching an AI security awareness initiative?

A. Publishing an AI acceptable use policy and collecting e-signatures of employees
B. Requiring employees to complete an annual generic phishing and deepfake awareness module
C. Delivering role-based and scenario-driven AI security training mapped to policy and job functions
D. Blocking access to public large language models (LLMs) at the network perimeter

Answer: C

Explanation:
AAISM prescribes targeted, role-based, scenario-driven training aligned to policy and job tasks as the highest-impact near-term intervention for human-factor AI risks. By mapping concrete
"do/don't" behaviors (e.g., what data may/may not be pasted into public chatbots, required redaction steps, approved tools, verification of outputs) to specific roles, organizations rapidly reduce incident likelihood and harmful actions.

NEW QUESTION # 62
Which of the following is the MOST likely cause of model drift?

A. Perfect knowledge
B. Membership inference
C. Model stealing
D. Data poisoning

Answer: D

Explanation:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data-centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high-likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from.

NEW QUESTION # 63
......

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