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ISACA AAISM Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: 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. Roles and responsibilities
3. Organizational structure and governance

- Frameworks and Compliance

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

Topic 2: AI Technologies and Controls

38%

- Data and Model Controls

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

- AI Assurance and Safety

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

- AI Security Architecture

1. Model lifecycle security
2. Secure AI system design

Topic 3: AI Risk Management

31%

- AI Threats and Vulnerabilities

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

- AI Risk Assessment

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
Which of the following should be the MOST important consideration when conducting an AI impact assessment?

A. Security awareness training
B. Achieve business objectives
C. Reputation of the organization
D. Effect on employee retention

Answer: B

Explanation:
AAISM defines an AI Impact Assessment (AIIA) as a structured evaluation designed to determine whether an AI system can safely and responsibly support business objectives without creating unacceptable risks.
The framework states that alignment to business objectives is the central purpose of AI adoption and therefore must be the starting point in an impact assessment.

NEW QUESTION # 34
When evaluating a new AI tool for intrusion prevention, which of the following is the MOST important consideration to ensure the tool fits within the existing program architecture?

A. Select a tool that integrates with the existing SIEM.
B. Confirm tool capabilities align with the control objectives.
C. Ensure automated response orchestration.
D. Prioritize a tool that offers real-time anomaly detection.

Answer: B

Explanation:
The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization' s established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance. While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to-and can be verified against-the control objectives that define the program's intended outcomes.
References:* AI Security Management™ (AAISM) Body of Knowledge: AI Governance and Program Management - Security program alignment, control objectives, and requirements traceability* AI Security Management™ Study Guide: Control objective mapping, architecture fit criteria, and solution selection governance

NEW QUESTION # 35
For a life insurance company deploying AI for fraud detection, which factor is MOST critical?

A. Robustness
B. Explainability
C. Adaptability
D. Accuracy

Answer: A

Explanation:
AAISM emphasizes robustness as the key requirement for fraud-detection systems because they must resist adversarial manipulation, data poisoning, spoofing, and input tampering.
Accuracy (B) matters but does not protect against adversarial attacks. Explainability (C) is important but secondary. Adaptability (D) is useful but not the top security requirement.
References: AAISM Study Guide - AI Robustness; Fraud Detection and Adversarial Resistance.

NEW QUESTION # 36
What BEST protects trade secrets related to AI technologies during their life cycle?

A. Restricting access to sensitive data
B. Patenting AI algorithms and data
C. Enforcing trademark rights
D. Watermarking AI output

Answer: A

Explanation:
AAISM emphasizes access control and data security as the strongest mechanisms to protect trade secrets, including:
* proprietary algorithms
* training datasets
* model weights
* internal design documentation
Trademarks (A) protect brand, not trade secrets. Patents (C) require public disclosure. Watermarks (D) protect generated content, not internal trade secrets.
References: AAISM Study Guide - AI Intellectual Property Protection & Access Controls.

NEW QUESTION # 37
An organization is adopting an agentic AI solution from an external vendor to support its internal IT operations. To evaluate the security posture of this system, which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?

A. Internal red team testing reports
B. General AI security whitepapers
C. Third-party audit reports
D. Industry benchmarking peer review

Answer: C

Explanation:
Third-party audit reports provide independent assurance that the vendor's stated controls are designed and operating effectively against recognized criteria. Such attestations (e.g., audit/assurance frameworks) are traceable, repeatable, and verifiable, and they support supply-chain risk reviews and contractual assurance.
Internal red-team reports are not independent, industry "peer reviews" are not control attestations, and whitepapers are marketing/educational materials without evidence of control operation.
References: AI Security Management (AAISM) Body of Knowledge: Third-Party & Supply-Chain Assurance; Independent Assessment and Audit Evidence; Vendor Risk Management. AAISM Study Guide:
Assurance Artifacts for AI Services; Evaluating External AI Providers.

NEW QUESTION # 38
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