AAISM全真問題集 & AAISM資格問題対応

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ISACA AAISM 認定試験の出題範囲：

トピック	出題範囲
トピック 1	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
トピック 2	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
トピック 3	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.

>> AAISM全真問題集 <<

AAISM資格問題対応 & AAISM資格難易度

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ISACA Advanced in AI Security Management (AAISM) Exam 認定 AAISM 試験問題 (Q329-Q334):

質問 # 329
The PRIMARY goal of data poisoning attacks is to:

A. compromise the confidentiality of output data from the model
B. manipulate the behavior of the model during development
C. compromise the confidentiality of model input data
D. undermine the integrity of the AI system's outputs

正解：D

解説：
AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine-tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production. While poisoning occurs during development/training (C), its primary objective is the downstream integrity impact on predictions/outputs (D). Options A and B relate to confidentiality threats (e.g., inversion or leakage), not poisoning.
References:* AI Security Management (AAISM) Body of Knowledge: Model Integrity Threats-data poisoning aims and effects; supply-side data controls* AAISM Study Guide: Risk scenarios for poisoning; mitigations via data provenance checks, robust training, and anomaly detection

質問 # 330
The BEST approach for aligning AI security requirements with the enterprise security program is to:

A. provide organization-wide training on ethical and responsible AI use.
B. embed security by design principles throughout the AI life cycle.
C. treat AI systems as standard IT assets and apply established security practices.
D. conduct periodic external audits to validate control alignment.

正解：B

解説：
Embedding security by design throughout the AI life cycle ensures that security requirements are integrated into planning, development, deployment, operation, and retirement activities from the beginning. This creates consistent alignment between AI initiatives and the enterprise security program while proactively addressing AI-specific risks.

質問 # 331
Which BEST describes the role of model cards in AI solutions?

A. They visualize AI model performance
B. They help developers create synthetic data
C. They document training data and AI model use cases
D. They automatically fine-tune AI models

正解：C

解説：
AAISM explains that model cards provide structured documentation about AI models, including:
* intended use cases
* training data characteristics
* ethical considerations
* known limitations
* risk factors
* performance benchmarks
They are not visualization tools (A), do not create synthetic data (C), and do not tune models (D).
References: AAISM Study Guide - AI Transparency & Model Cards.

質問 # 332
From a risk perspective, which of the following is the MOST important step when implementing an adoption strategy for AI systems?

A. Benchmarking against peer organizations' AI risk strategies
B. Establishing a comprehensive AI risk assessment framework
C. Implementing a robust risk analysis methodology tailored to AI-specific tasks
D. Conducting an AI risk assessment and updating the enterprise risk register

正解：D

解説：
AAISM guidance states that when adopting AI, the most important step is to conduct a risk assessment and update the enterprise risk register. This ensures AI-specific risks are identified, documented, and integrated into the organization's existing governance structures. Benchmarking peers provides context but does not address internal risk. Implementing methodologies and frameworks are important, but they precede or follow the assessment process. The decisive step that connects adoption to enterprise risk governance is updating the risk register with AI-specific risks.
References:
AAISM Study Guide - AI Risk Management (Integration with Enterprise Risk Management) ISACA AI Security Management - Risk Assessment and Register Updates

質問 # 333
An attacker used model poisoning to insert a backdoor into an open-source AI model, causing a data breach. Which of the following is the FIRST action to prevent reoccurrence?

A. Perform a root cause analysis, secure the supply chain, and validate model outputs.
B. Isolate the AI system, retrain the source model, and secure the data pipelines.
C. Implement a security orchestration, automation, and response (SOAR) solution.
D. Engage legal counsel, implement regulatory monitoring, and update the response plan.

正解：A

解説：
Performing a root cause analysis is the first priority because it identifies how the poisoning attack occurred and where controls failed. Securing the supply chain and validating model outputs directly address the compromised open-source model and help prevent similar backdoor insertions in future deployments.

質問 # 334
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