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[General] AAISM日本語練習問題 & AAISM学習指導

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【General】 AAISM日本語練習問題 & AAISM学習指導

Posted at 13 hour before      View：8 | Replies：0        Print      Only Author   [Copy Link] 1#
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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 リスク管理: 試験のこのセクションでは、AI リスク管理者のスキルを測定し、リスク処理計画やベンダー監視など、AI 導入に関連する企業の脅威、脆弱性、サプライ チェーン リスクの評価をカバーします。
トピック 2
  • AI ガバナンスとプログラム管理: 試験のこのセクションでは、AI セキュリティ ガバナンス プロフェッショナルの能力を測定し、ガバナンス フレームワーク、ポリシー作成、データ ライフサイクル管理、プログラム開発、インシデント対応プロトコルを通じて AI セキュリティを実装する際に関係者にアドバイスすることに重点を置いています。
トピック 3
  • AIテクノロジーとコントロール：このセクションでは、AIセキュリティアーキテクトの専門知識を測定し、安全なAIアーキテクチャとコントロールの設計に関する知識を評価します。プライバシー、倫理、信頼に関する懸念事項、データ管理コントロール、監視メカニズム、そしてAIシステムに合わせたセキュリティコントロールの実装について扱います。

ISACA Advanced in AI Security Management (AAISM) Exam 認定 AAISM 試験問題 (Q57-Q62):質問 # 57
An organization has discovered that employees have started regularly utilizing open-source generative AI without formal guidance. Which of the following should be the CISO's GREATEST concern?
  • A. Lack of monitoring
  • B. Model hallucinations
  • C. Data leakage
  • D. Policy violations
正解：C
解説：
The greatest immediate risk from unsanctioned use of public or open-source generative AI tools is data leakage-employees may paste confidential or regulated information into third-party systems, resulting in loss of confidentiality, regulatory exposure, and loss of intellectual property. AAISM emphasizes that when AI use occurs outside approved channels, the top control priority is preventing exfiltration of sensitive data via prompts, attachments, and context sharing. Monitoring and policy are necessary enablers, but leakage is the highest-impact failure mode in the short term; hallucinations primarily affect accuracy, not confidentiality.
References:* AI Security Management (AAISM) Body of Knowledge: Generative AI governance; human- in-the-loop risks; data loss and exfiltration vectors in prompts; sanctioned vs. unsanctioned AI usage.* AI Security Management Study Guide: Immediate risk triage for shadow AI; DLP and input-control safeguards; confidentiality-first posture for generative AI adoption.

質問 # 58
An aerospace manufacturing company that prioritizes accuracy and security has decided to use generative AI to enhance operations. Which of the following large language model (LLM) adoption plans BEST aligns with the company's risk appetite?
  • A. Purchasing an LLM dataset on the open market
  • B. Developing a public LLM to automate critical functions
  • C. Developing a private LLM to automate non-critical functions
  • D. Contracting LLM access from a reputable third-party provider
正解：C
解説：
AAISM recommends aligning AI adoption with organizational risk appetite by limiting blast radius, protecting sensitive data, and staging adoption in lower-risk domains first. Building a private LLM for non-critical functions preserves data control, enables tighter governance (access control, logging, evaluation), and confines any model errors away from safety- or mission-critical operations. A public LLM for critical functions (A) is misaligned with a high-assurance posture; buying open-market datasets (B) raises provenance and licensing risk; third-party access (C) can be appropriate but still introduces vendor/visibility limits and data residency concerns that may not meet aerospace security needs.
References: AI Security Management (AAISM) Body of Knowledge - Risk Appetite Mapping to AI Use Cases; Criticality Segmentation; Data Control & Deployment Models. AAISM Study Guide - Phased Adoption for High-Assurance Environments; Private vs. Hosted LLM Trade-offs; Governance, Evaluation, and Containment Patterns.

質問 # 59
Which of the following AI data life cycle phases presents the GREATEST inherent risk?
  • A. Preparation
  • B. Monitoring
  • C. Training
  • D. Maintenance
正解：A
解説：
The data Preparation phase-covering sourcing, collection, labeling, cleansing, and provenance-presents the greatest inherent risk because it is where privacy, consent, representativeness, bias, quality, lineage, and legality must be established. Decisions and defects here propagate into training and downstream use, amplifying ethical, regulatory, and accuracy risks.
While Training can introduce additional risks (e.g., poisoning, leakage), these are frequently mitigated by controls that depend on having trustworthy prepared data. Monitoring and Maintenance are later-life-cycle phases oriented toward detection and correction; they are critical but inherently rely on the foundation set during Preparation.
References: AI Security Management™ (AAISM) Body of Knowledge: "AI Data Lifecycle Risks-Sourcing, Consent, Provenance, and Bias," "Risk Treatment Priorities Across the Lifecycle"; AAISM Study Guide:
"Data Preparation Controls and Quality Gates," "Bias and Privacy Risk Controls at Ingestion."

質問 # 60
Which of the following methods provides the MOST effective protection against model inversion attacks?
  • A. Increasing the number of training iterations
  • B. Using adversarial training
  • C. Reducing the model's complexity
  • D. Implementing regularization output
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 # 61
An organization is deploying an automated AI cybersecurity system. Which of the following would be the MOST effective strategy to minimize human error and improve overall security?
  • A. Conducting periodic penetration testing
  • B. Using historical data to train AI detection software
  • C. Utilizing machine learning (ML) algorithms to ensure responsible use
  • D. Implementing manual monitoring of potential alerts
正解：B
解説：
Training detection models on relevant, representative historical data improves signal quality, reduces false positives, and automates triage-directly lowering human workload and error rates (e.g., alert fatigue, missed correlations). Penetration testing is valuable but episodic and does not systematically reduce day-to-day operator error. "Ensure responsible use" is a governance aim, not a concrete method to cut human error in detection. Manual monitoring increases reliance on human judgment and is prone to inconsistency.
References: AI Security Management™ (AAISM) Body of Knowledge: Model Development & Evaluation Controls; Data Selection and Representativeness; Operationalization to Reduce Human Error. AAISM Study Guide: Tuning Detection Systems with Historical Corpora; Alert Quality, Precision/Recall, and SOC Workflow Integration.

質問 # 62
......
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