참고: Itexamdump에서 Google Drive로 공유하는 무료 2026 ISACA AAISM 시험 문제집이 있습니다: https://drive.google.com/open?id=1Rr2vxdwufoCuM2IBVBqPW17JVaHZkSqv
Itexamdump의ISACA AAISM 덤프 구매 후 등록된 사용자가 구매일로부터 일년 이내에ISACA AAISM시험에 실패하셨다면 Itexamdump메일에 주문번호와 불합격성적표를 보내오셔서 환불신청하실수 있습니다.구매일자 이전에 발생한 시험불합격은 환불보상의 대상이 아닙니다. 개별 인증사는 불합격성적표를 발급하지 않기에 재시험신청내역을 환불증명으로 제출하시면 됩니다.
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발달한 네트웨크 시대에 인터넷에 검색하면 많은ISACA인증 AAISM시험공부자료가 검색되어 어느 자료로 시험준비를 해야 할지 망서이게 됩니다. 이 글을 보는 순간 다른 공부자료는 잊고Itexamdump의ISACA인증 AAISM시험준비 덤프를 주목하세요. 최강 IT전문가팀이 가장 최근의ISACA인증 AAISM 실제시험 문제를 연구하여 만든ISACA인증 AAISM덤프는 기출문제와 예상문제의 모음 공부자료입니다. Itexamdump의ISACA인증 AAISM덤프만 공부하면 시험패스의 높은 산을 넘을수 있습니다.
질문 # 115
Which of the following technologies can be used to manage deepfake risk?
정답：A
설명：
The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks. Blockchain's immutability and traceability make it the recognized technology for mitigating deepfake challenges.
References:
AAISM Study Guide - AI Technologies and Controls (Emerging Controls for Content Authenticity) ISACA AI Governance Guidance - Blockchain for Data Integrity and Deepfake Mitigation
질문 # 116
An AI fraud detection system frequently flags legitimate transactions as suspicious, leading to customer dissatisfaction and operational obstacles. Which of the following is the BEST way to mitigate this issue without compromising security?
정답：B
설명：
Conducting a model review helps identify why legitimate transactions are being incorrectly flagged and enables improvements to model accuracy. Adding explainability techniques also helps analysts and stakeholders better understand decision outcomes, reducing operational issues while maintaining effective fraud detection capabilities.
질문 # 117
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?
정답：D
설명：
AAISM's approach to third-party and vendor risk for AI systems stresses data usage transparency as a primary control. The guidance explains that organizations must obtain clear documentation on "what data is collected, how it is processed, stored, retained, and whether it is reused for training or shared with other parties." Option C directly addresses this by requiring the vendor to disclose how the application uses organizational data, enabling appropriate risk assessment, contractual controls, and technical safeguards. An external audit (A) can be useful but may be costly and not always feasible pre-procurement. Legal discussions (B) are important but ineffective without clarity on data flows. Publicly available policies (D) are often high- level and marketing-oriented, lacking the specificity required for proper risk evaluation. Therefore, obtaining explicit data usage disclosures from the vendor is the most effective starting point.
References: AI Security Management™ (AAISM) Study Guide - Third-Party AI Risk and Data Sharing; Vendor Governance Requirements.
질문 # 118
A financial institution plans to deploy an AI system to provide credit risk assessments for loan applications.
Which of the following should be given the HIGHEST priority in the system's design to ensure ethical decision-making and prevent bias?
정답：C
설명：
In AI governance frameworks, credit scoring is treated as a high-risk application. For such systems, the highest-priority safeguard is human oversight to ensure fairness, accountability, and prevention of bias in automated decisions.
The AI Security Management (AAISM) domain of AI Governance and Program Management emphasizes that high-impact AI systems require explicit governance structures and human accountability. Human-in-the- loop design ensures that final decisions remain the responsibility of human experts rather than being fully automated. This is particularly critical in financial contexts, where biased outputs can affect individuals' access to credit and create compliance risks.
Official ISACA AI governance guidance specifies:
High-risk AI systems must comply with strict requirements, including human oversight, transparency, and fairness.
The purpose of human oversight is to reduce risks to fundamental rights by ensuring humans can intervene or override an automated decision.
Bias controls are strengthened by requiring human review processes that can analyze outputs and prevent unfair discrimination.
Why other options are not the highest priority:
A). Regular updates improve accuracy but do not guarantee fairness or ethical decision-making. Model drift can introduce new bias if not governed properly.
B). Appeals mechanisms are important for accountability, but they operate after harm has occurred.
Governance frameworks emphasize prevention through human oversight in the decision loop.
D). Restricting criteria to "objective metrics" is insufficient, as even objective data can contain hidden proxies for protected attributes. Bias mitigation requires monitoring, testing, and human oversight, not only feature restriction.
AAISM Domain Alignment:
Domain 1 - AI Governance and Program Management: Ensures accountability, ethical oversight, and governance structures.
Domain 2 - AI Risk Management: Identifies and mitigates risks such as bias, discrimination, and lack of transparency.
Domain 3 - AI Technologies and Controls: Provides the technical enablers for implementing oversight mechanisms and bias detection tools.
References from AAISM and ISACA materials:
AAISM Exam Content Outline - Domain 1: AI Governance and Program Management (roles, responsibilities, oversight).
ISACA AI Governance Guidance (human oversight as mandatory in high-risk AI applications).
Bias and Fairness Controls in AI (human review and intervention as a primary safeguard).
질문 # 119
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：C
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.
References:
AAISM Exam Content Outline - AI Risk Management (Legal and Intellectual Property Risks) AI Security Management Study Guide - Generative AI Data Governance
질문 # 120
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지금 같은 경쟁력이 심각한 상황에서ISACA AAISM시험자격증만 소지한다면 연봉상승 등 일상생활에서 많은 도움이 될 것입니다.ISACA AAISM시험자격증 소지자들의 연봉은 당연히ISACA AAISM시험자격증이 없는 분들보다 높습니다. 하지만 문제는ISACA AAISM시험패스하기가 너무 힘듭니다. Itexamdump는 여러분의 연봉상승을 도와 드리겠습니다.
AAISM인증시험공부: https://www.itexamdump.com/AAISM.html
참고: Itexamdump에서 Google Drive로 공유하는 무료, 최신 AAISM 시험 문제집이 있습니다: https://drive.google.com/open?id=1Rr2vxdwufoCuM2IBVBqPW17JVaHZkSqv