AAISM덤프문제모음 - AAISM최신시험기출문제모음

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ISACA AAISM 시험요강:

주제	소개
주제 1	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.
주제 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.
주제 3	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.

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AAISM최신 시험 기출문제 모음 - AAISM시험패스 인증덤프자료

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최신 ISACA AAISM Certification AAISM 무료샘플문제 (Q92-Q97):

질문 # 92
Which of the following should be the MOST important consideration when conducting an AI impact assessment?

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

정답：B

설명：
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.

질문 # 93
Which of the following is the MOST effective and secure way to prevent model attribute attacks against an organization's centralized AI system training data that includes sensitive information about individuals?

A. Monitoring the output of AI learning models continuously
B. Implementing a cross-validation AI learning model
C. Removing private data from AI training datasets
D. Using federated learning to train AI algorithms

정답：D

설명：
Federated learning keeps sensitive data decentralized by training models locally and sharing only model updates rather than raw data. This significantly reduces exposure of individual-level information and helps prevent attribute inference attacks against centralized training datasets.

질문 # 94
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?

A. Applying threat modeling specific to AI components before deployment
B. Integrating differential privacy mechanisms into model training to limit data leakage
C. Restricting access to AI models using IP allow lists to reduce public exposure
D. Segmenting AI services across containers to manage resource constraints

정답：A

설명：
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.
References: AI Security Management (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.

질문 # 95
A large financial institution is integrating a third-party AI solution into its fraud detection system. Which is the BEST way to reduce AI vendor/supply chain risk?

A. Establish contractual agreements requiring evidence of secure development practices
B. Use isolated virtual environments to validate integration
C. Focus on performance testing
D. Conduct annual vulnerability assessments after integration

정답：A

설명：
AAISM emphasizes contractual governance controls as the strongest mechanism for managing AI vendor and supply chain dependencies. Contracts must require:
* secure development lifecycle practices
* documentation of controls
* security testing
* supply chain transparency
Isolated environments (C) help, but they do not ensure upstream integrity. Performance testing (D) is unrelated to security. Annual assessments (A) occur too late to mitigate onboarding risks.
References: AAISM Study Guide - AI Vendor Governance; Contractual Risk Controls.

질문 # 96
Which BEST describes the role of model cards in AI solutions?

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

정답：A

설명：
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.

질문 # 97
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