ISACA AAISM無料試験、AAISM試験

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ISACA AAISM Exam Syllabus Topics:

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

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ISACA Advanced in AI Security Management (AAISM) Exam 認定 AAISM 試験問題 (Q34-Q39):

質問 # 34
An organization is implementing an AI-based credit assessment engine using internal and third-party customer data. Which of the following BEST aligns with data management controls for the AI life cycle?

正解：D

解説：
The strongest alignment with AI life-cycle data management controls is establishing documented procedures for data sourcing, lineage tracking, and quality validation. Governance requires end-to-end data controls- provenance, consent, sourcing approvals, lineage, quality checks, labeling standards, retention, and auditability-so models are trained and validated on trustworthy, compliant data. Hashing alone is pseudo- anonymization and does not deliver full governance. Restricting to structured data is an implementation constraint, not a control set. Encryption and access control protect confidentiality/availability but do not ensure lineage or quality.
References: AI Security Management (AAISM) Body of Knowledge: Data Governance & Stewardship; AI Data Lifecycle Controls; Data Provenance, Lineage, and Quality Assurance. AAISM Study Guide: Data Intake Procedures; Lineage and Audit Evidence; Quality Gates and Approval Workflows.


質問 # 35
Which of the following is MOST appropriate to assess the risk of a new AI model being compromised when encountering inputs not represented in the training data?

正解：D

解説：
Out-of-distribution detection is specifically designed to identify inputs that differ significantly from the data used during model training. This helps assess and mitigate the risk that the AI model may behave unpredictably, inaccurately, or insecurely when exposed to unfamiliar inputs.


質問 # 36
Which of the following would BEST protect trade secrets related to AI technologies during their life cycle?

正解：B

解説：
Restricting access to sensitive data and artifacts (e.g., training data, feature stores, model weights, prompts, system designs) using least-privilege, segregation, encryption, and monitoring is the most effective way to protect trade secrets throughout the AI lifecycle. Patents require public disclosure, trademarks protect branding (not secrets), and output watermarks help provenance/abuse deterrence but do not secure underlying proprietary know-how.
References: AI Security Management™ (AAISM) Body of Knowledge: Information Protection for AI- Access Control, Segmentation, and Secrets Management; AAISM Study Guide: Lifecycle Security of AI Artifacts and Trade-Secret Safeguards.


質問 # 37
Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?

正解：B

解説：
The most effective strategy tominimize attack surfacesin AI agent security is to apply compartmentalization and least privilege enforcement.
AAISM control frameworks emphasize:
* Isolation of components (e.g., training, inference, data pipelines) to limit lateral movement.
* Principle ofleast privilegeto restrict access only to what is required for function.
* Hardening AI pipelines through segmentation rather than relying solely on manual reviews or monitoring.
Pre-trained models and log centralization are useful but do not directly reduce the attack surface.Manual code reviewsare important but insufficient against runtime exploitation.
Thus,compartmentalization with least privilege enforcementis the most effective technical safeguard.


質問 # 38
Which of the following methods provides the MOST effective protection against model inversion attacks?

正解：D

解説：
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
* A (adversarial training) targets perturbation robustness, not primary for inversion.
* B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
* D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.* AI Security Management™ Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.


質問 # 39
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