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IT국제공인자격증ISACA AAISM시험대비덤프를 제공하는 전문적인 사이트로서 회원님의 개인정보를 철저하게 보호해드리고 페이팔을 통한 결제라 안전한 결제를 진행할수 있습니다. ISACA AAISM 덤프외에 다른 인증시험덤프에 관심이 있으신 분은 온라인 서비스를 클릭하여 문의해주세요.
질문 # 21
Which of the following controls BEST mitigates the inherent limitations of generative AI models?
정답：A
설명：
The AAISM governance framework emphasizes that the inherent limitations of generative AI-including hallucinations, bias, and unpredictability-are best mitigated by human oversight. Human-in-the-loop review ensures that outputs are validated before being used in sensitive or high-risk contexts. Regulatory adoption, system classification, and reverse engineering all play supporting roles but do not directly safeguard against the model's inherent unpredictability. Governance best practices highlight human oversight as the critical safeguard.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Human Oversight and Accountability) AI Security Management Study Guide - Mitigating Generative AI Limitations
질문 # 22
Which of the following would BEST protect trade secrets related to AI technologies during their life cycle?
정답：A
설명：
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.
질문 # 23
Which of the following recommendations would BEST help a service provider mitigate the risk of lawsuits arising from generative AI's access to and use of internet data?
정답：A
설명：
The AAISM materials highlight that one of the primary legal risks with generative AI systems is the unauthorized use of copyrighted or intellectual property-protected data drawn from internet sources. To mitigate lawsuits, the most effective recommendation is to implement filtering logic that actively excludes data flagged for intellectual property risks before ingestion or generation.
While disclosing compliance policies, appointing governance roles, or reviewing logs are supportive measures, they do not directly prevent the core liability of using restricted content. The study guide explicitly emphasizes that proactive filtering and data governance controls are the most effective safeguards against legal disputes concerning content origin.
질문 # 24
From a risk perspective, which of the following is the MOST important step when implementing an adoption strategy for AI systems?
정답：D
설명：
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
질문 # 25
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?
정답：A
설명：
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.
질문 # 26
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