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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 thresholds and treatment 2. Risk identification and analysis
AI Governance and Program Management	31%	- AI Strategy and Policy 1. AI strategy development 2. Policies and procedures 3. Responsible and acceptable use - Frameworks and Compliance 1. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001) 2. Privacy and ethics considerations 3. Regulatory requirements - AI Governance Foundations 1. Stakeholder considerations 2. Roles and responsibilities 3. Organizational structure and governance
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. Bias mitigation 2. Ethical AI controls 3. Model validation and testing

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 thresholds and treatment
2. Risk identification and analysis

AI Governance and Program Management

31%

- AI Strategy and Policy

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

- Frameworks and Compliance

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

- AI Governance Foundations

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

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q246-Q251):

NEW QUESTION # 246
When using AI as part of incident response, which of the following BEST ensures the automation aligns with regulatory and governance obligations?

A. Train the AI incident response platform to mirror legacy response workflows and log containment
B. Apply anomaly detection models to filter incoming threats and automate containment
C. Implement a tiered automation strategy where severity ratings inform the need for human oversight
D. Use deep learning models to autonomously classify all incidents

Answer: C

Explanation:
AAISM prescribes risk-based, human-in-the-loop orchestration for safety-critical or regulated actions. A tiered automation strategy that gates autonomy by incident severity, data sensitivity, and regulatory requirements ensures accountability, auditability, and proportionality, satisfying governance obligations.

NEW QUESTION # 247
When evaluating a third-party AI service provider, which of the following master services agreement provisions is MOST critical for managing security risk?

A. Restricting query volume thresholds
B. Prohibiting the use of customer data for model training
C. Sharing real-time log information
D. Guaranteeing unlimited model retraining requests

Answer: B

Explanation:
The most material contractual control for reducing security and privacy risk in outsourced AI services is a data-use restriction that prohibits the provider from using customer data for model training (and from derivative model improvements) unless explicitly authorized. This prevents unintended secondary processing, model inversion exposure of proprietary data, unauthorized profiling, and downstream data proliferation across multi-tenant systems. AAISM positions third-party risk controls to prioritize data minimization, purpose limitation, confidentiality, and downstream controls; among common MSA provisions, data-use limitations directly constrain the provider's technical and organizational handling of sensitive inputs, making it the highest-impact risk-reducing clause. Query throttling (B) and logging (C) are useful operational controls but are secondary to legal/processing authority. Unlimited retraining (D) increases attack surface and cost without addressing the core risk of misuse of customer data.
References: AI Security Management (AAISM) Body of Knowledge - Third-Party & Supply-Chain Governance; Contractual Controls for AI Services; Data Minimization and Purpose Limitation. AAISM Study Guide - Procurement & MSA/DPA Clauses for AI; Provider Model Training and Data-Use Restrictions; Privacy & Confidentiality Safeguards in Outsourced AI.

NEW QUESTION # 248
The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:

A. align the system components of AI with the business goals of the organization
B. provide a basis for identification of threats and vulnerabilities
C. ensure the development of powerful, efficient, and scalable AI systems
D. deploy fast and cost-efficient AI systems for rapidly changing environments

Answer: A

Explanation:
An AI architecture, within program governance, exists to align AI system components and lifecycle processes with business goals and policy constraints. Architecture provides the organizing structure linking strategy, capabilities, processes, data, models, controls, and assurance so that AI outcomes are traceable to business value, risk appetite, and compliance expectations. Efficiency, speed, and threat analysis are important architectural qualities, but they are not the primary purpose; the primary purpose is strategic and governance alignment so that technical choices and controls consistently realize organizational objectives.
References:* AI Security Management (AAISM) Body of Knowledge: AI Program Architecture - alignment of capabilities, processes, and controls to business objectives* AI Security Management Study Guide: Architecture-driven governance, traceability from business goals to technical and control design

NEW QUESTION # 249
Which of the following is the GREATEST benefit of performing AI security risk assessments?

A. Risk prioritization decisions are made for AI security
B. The risk register is updated with the latest AI risk
C. Appropriate privacy risk controls are implemented for AI models
D. The appropriate level of funding is secured for AI security risk

Answer: A

Explanation:
AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls. Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Assessment & Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.

NEW QUESTION # 250
An organization is adopting multiple AI solutions for customer support and management is concerned about potential privacy and confidentiality violations due to the implementation of the new solution. Which of the following is the BEST risk mitigation strategy in this situation?

A. Use consumer-level AI guidelines with rules, terms, and recommendations for end users.
B. Rely on best practice manuals from AI vendors to guide how employees use the systems.
C. Create responsible AI use policies that address ethics, acceptable use, and transparency.
D. Implement key risk indicators (KRIs) to monitor AI usage and update the risk register.

Answer: C

Explanation:
Establishing responsible AI use policies defines clear expectations for handling data, ethical use, and transparency, directly addressing privacy and confidentiality risks across multiple AI solutions and guiding consistent behavior organization-wide.

NEW QUESTION # 251
......

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