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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Technologies and Controls38%- Trust, safety and compliance controls
  • 1. Responsible AI practices
  • 2. Compliance and governance validation
- Data management and privacy controls
  • 1. Data integrity and protection
  • 2. Privacy and ethical AI controls
- AI security architecture and design
  • 1. Secure AI system architecture
  • 2. AI infrastructure protection
- AI security monitoring and operations
  • 1. Security monitoring and logging
  • 2. Operational AI controls
Topic 2: AI Risk Management31%- AI threat and vulnerability management
  • 1. AI vulnerability assessment
  • 2. Threat modeling for AI systems
- AI risk assessment and treatment
  • 1. Risk identification and analysis
  • 2. Risk thresholds and mitigation
- Third-party and supply chain risk management
  • 1. Vendor risk evaluation
  • 2. Supply chain security controls
Topic 3: AI Governance and Program Management31%- Stakeholder considerations and governance frameworks
  • 1. Industry frameworks and regulatory requirements
  • 2. AI governance models and oversight
- AI asset and data lifecycle management
  • 1. Data governance
  • 2. AI asset inventory and management
- AI security program management
  • 1. Program development and maintenance
  • 2. Incident response and business continuity
- AI policies and procedures
  • 1. AI-related strategy development
  • 2. Policy implementation and compliance

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q411-Q416):

NEW QUESTION # 411
Which of the following is MOST important for an organization to consider when implementing a preventive security safeguard into a new AI product?

Answer: C

Explanation:
AAISM materials emphasize that the most effective preventive safeguard is to ensure input sanitization. Preventive controls stop malicious or malformed inputs from reaching the model in the first place, thereby reducing the likelihood of prompt injection, evasion, or poisoning at inference time. Model output monitoring is a detective control, not preventive. Penetration testing is an assessment technique rather than a safeguard. Differential privacy protects data privacy but does not prevent adversarial input manipulation. Therefore, the most important preventive safeguard in a new AI product is robust input sanitization.


NEW QUESTION # 412
Which of the following MOST effectively addresses bias in generative AI models?

Answer: D

Explanation:
AAISM identifies fairness constraints as a direct mechanism to mitigate and control model bias by embedding fairness requirements into optimization objectives during training.
Data augmentation (B) helps but is not a primary anti-bias control. Adversarial training (C) focuses on robustness, not fairness. Minimization (A) reduces data, often making bias worse.
References: AAISM Study Guide - Fairness, Bias Mitigation Techniques, Ethical AI Controls.


NEW QUESTION # 413
Which of the following BEST describes the role of transparency in AI?

Answer: B

Explanation:
Transparency in AI is a governance principle requiring that systems be explainable to stakeholders in ways that are understandable and meaningful, enabling clear articulation of how decisions were reached and why.
Within an AI program, transparency supports accountability, auditability, and trust by ensuring that reasons for decisions can be communicated and scrutinized. Option C reflects this definition by focusing on intelligible, logical explanations of system behavior and decision rationale.
Option A is a narrow technique (model-specific interpretability for decision trees) and does not capture transparency as a broad governance requirement. Option B conflates transparency with full public disclosure; transparency does not require making all artifacts openly available. Option D is persuasion/advocacy, not transparency.
References: AI Security Management (AAISM) Body of Knowledge: "AI Governance-Transparency and Explainability," "Accountability and Assurance"; AAISM Study Guide: "Explainability Objectives and Stakeholder Communication," "Documentation for Decision Rationale."


NEW QUESTION # 414
Which of the following is the MOST effective way to identify and address security risk in an AI model?

Answer: A

Explanation:
AI/ML threat modeling is the most effective structured method to both identify and address model security risks. It systematically surfaces attack classes (poisoning, evasion, membership inference, model extraction, inversion), maps system-specific attack surfaces (data pipelines, feature stores, training artifacts, inference APIs), and drives prioritized mitigations (ingestion validation, robust training, rate-limiting, watermarking, differential privacy, monitoring, red teaming). Output spot-checking (A) finds errors but not security vulnerabilities; encryption (C) protects confidentiality but does not reveal threats or mitigate inference-time attacks; adding data (D) may improve accuracy but does not target adversarial risk.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Risk Identification & Threat Modeling; Attack Surface Analysis for ML; Risk Treatment Planning. AAISM Study Guide - Evasion
/Poisoning/Extraction Controls; Mapping Risks to Controls; Validation and Assurance Activities.


NEW QUESTION # 415
An organization plans to leverage AI in the software development process to speed up coding.
Which of the following should the information security manager do FIRST?

Answer: B

Explanation:
AAISM guidance specifies that before introducing AI into any business or technical workflow, an AI Impact Assessment must be conducted early to determine potential risks, privacy implications, misuse scenarios, governance gaps, and required security controls. This aligns with the principle that AI adoption must begin with governance and risk identification, not training or policy modification.


NEW QUESTION # 416
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