2025年最新のの検証済みAAISM問題と解答で合格保証 もしくは全額返金
[2025年12月]更新のAAISM認証と実際の解答はここにあるGoShiken
ISACA AAISM 認定試験の出題範囲：
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
質問 # 50
Which of the following AI data management techniques involves creating validation and test data?
- A. Training
- B. Splitting
- C. Annotating
- D. Learning
正解：B
解説：
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.
質問 # 51
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?
- A. Implementing access controls to protect the AI system from unauthorized use
- B. Increasing the detail of AI solution backup and restoration processes
- C. Testing the AI infrastructure failover mechanisms
- D. Enhancing monitoring and detection of model failures and anomalies
正解：C
解説：
Effective AI BCP requires validation through exercises and controlled failover tests to prove recovery objectives can be met in practice. Merely documenting backups (Option D), hardening access (Option B), or improving monitoring (Option A) does not confirm that the AI stack-data pipelines, feature stores, model registries, inference services, and dependent infrastructure-can actually fail over and recover within RTO
/RPO. AAISM prescribes periodic BCP/DR testing (including model artifact restoration, configuration reconstitution, dependency failover, and data pipeline continuity) to verify readiness and identify gaps before real incidents.
References:AI Security Management (AAISM) Body of Knowledge: Business Continuity & Disaster Recovery for AI; Validation and Exercising of Continuity Plans; RTO/RPO for Models, Data, and Pipelines.
AAISM Study Guide: Operational Resilience for AI Systems; BCP/DR Test Scenarios (model registry, feature store, pipeline recovery); Continuity Metrics and Evidence of Readiness.
質問 # 52
Which of the following should be done FIRST when developing an acceptable use policy for generative AI?
- A. Consult with risk management and legal
- B. Determine the scope and intended use of AI
- C. Review AI regulatory requirements
- D. Review existing company policies
正解：B
解説：
According to the AAISM framework, the first step in drafting an acceptable use policy is defining the scope and intended use of the AI system. This ensures that governance, regulatory considerations, risk assessments, and alignment with organizational policies are all tailored to the specific applications and functions the AI will serve. Once scope and intended use are clearly defined, legal, regulatory, and risk considerations can be systematically applied. Without this step, policies risk being generic and misaligned with business objectives.
References:
AAISM Study Guide - AI Governance and Program Management (Policy Development Lifecycle) ISACA AI Governance Guidance - Defining Scope and Use Priorities
質問 # 53
Which of the following is the GREATEST benefit of performing AI security risk assessments?
- A. Appropriate privacy risk controls are implemented for AI models
- B. The risk register is updated with the latest AI risk
- C. The appropriate level of funding is secured for AI security risk
- D. Risk prioritization decisions are made for AI security
正解：D
解説：
AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls. Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Assessment & Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.
質問 # 54
Which of the following AI-driven systems should have the MOST stringent recovery time objective (RTO)?
- A. Car navigation system
- B. Credit risk modeling system
- C. Health support system
- D. Industrial control system
正解：D
解説：
AAISM risk guidance notes that the most stringent recovery objectives apply to industrial control systems, as downtime can directly disrupt critical infrastructure, manufacturing, or safety operations. Health support systems also require high availability, but industrial control often underpins safety-critical and real-time environments where delays can result in catastrophic outcomes. Credit risk models and navigation systems are important but less critical in terms of immediate physical and operational impact. Thus, industrial control systems require the tightest RTO.
References:
AAISM Study Guide - AI Risk Management (Business Continuity in AI)
ISACA AI Security Management - RTO Priorities for AI Systems
質問 # 55
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?
- A. Update governance policies based on lessons learned
- B. Ensure a certificate of destruction is received and archived
- C. Perform a post-destruction risk assessment
- D. Ensure backups are tested and access controls are audited
正解：B
解説：
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
* proof of lawful data disposal
* auditability
* regulatory compliance
* defensibility during inspections
Post-destruction risk assessments (A) are not primary compliance evidence. Backup tests (B) are operational tasks, not decommissioning proof. Policy updates (C) are future improvements.
References: AAISM Study Guide - AI Decommissioning & Regulatory Compliance; Data Destruction Evidence.
質問 # 56
When robust input controls are not practical on a large language model (LLM) to prevent prompt injection attacks from external threats, which of the following would be the BEST compensating control to address the risk?
- A. Conduct human reviews of the AI system's inputs
- B. Implement identity and access management (IAM)
- C. Fine-tune the system to validate the AI system's inputs
- D. Review and annotate the AI system's outputs
正解：D
解説：
When preventive input hardening isn't feasible for LLMs, AAISM prescribes compensating detective and corrective controls-notably human review and annotation of outputs prior to downstream action-to reduce harm from prompt injection. Output-side review gates prevent untrusted instructions from propagating, enable rapid suppression/feedback loops, and provide labeled examples for subsequent model hardening. IAM (B) is necessary but does not mitigate injection in content; reviewing inputs (C) is less effective than auditing what the model is about to act on; fine-tuning for validation (D) is helpful long-term but is not an immediate compensating control when robust input validation is impractical.
References: AI Security Management (AAISM) Body of Knowledge - LLM Threats & Compensating Controls; Human Oversight & Output Review Gates; Post-incident Feedback and Labeling for Model Hardening.
質問 # 57
Which of the following BEST describes an adversarial attack on an AI model?
- A. Providing inputs that mislead the model into incorrect predictions
- B. Attacking underlying hardware
- C. Reverse-engineering the model using social engineering
- D. Conducting denial-of-service attacks on AI APIs
正解：A
解説：
AAISM defines adversarial attacks as manipulations of input data (text, image, audio, numeric values) designed to cause the model to produce incorrect or harmful predictions.
Hardware attacks (A) are infrastructure threats. Social engineering (C) targets people, not models. DoS attacks (D) affect availability, not model decision pathways.
References: AAISM Study Guide - Adversarial Threats; Input Manipulation.
質問 # 58
A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
Which of the following attacks is the HIGHEST priority to protect against?
- A. Data poisoning
- B. Model inversion
- C. Unauthorized tuning
- D. Model distillation
正解：B
解説：
AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
References:* AI Security Management (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces
質問 # 59
Which of the following is the MOST critical key risk indicator (KRI) for an AI system?
- A. The response time of the model
- B. The amount of data in the model
- C. The rate of drift in the model
- D. The accuracy rate of the model
正解：C
解説：
AAISM highlights that while accuracy and performance metrics are important, the rate of drift is the most critical KRI for AI systems. Model drift occurs when input data or environmental conditions shift, causing the system to degrade and produce unreliable outputs. This risk indicator directly reflects whether the AI continues to function as intended over time. Accuracy rates and response times are performance metrics, not primary risk signals. The amount of data in the model does not reliably indicate exposure to risk. Therefore, the greatest KRI for ongoing assurance and governance is the rate of drift.
References:
AAISM Study Guide - AI Risk Management (Monitoring and Drift Detection) ISACA AI Security Management - Key Risk Indicators for AI Systems
質問 # 60
Which of the following would BEST help to prevent the compromise of a facial recognition AI system through the use of alterations in facial appearance?
- A. Implementing a secondary AI system to confirm images
- B. Enhancing training data to increase variance
- C. Monitoring the system for misuse cases
- D. Fine-tuning the AI model to decrease hallucinations
正解：B
解説：
AAISM materials note that adversaries may attempt to bypass facial recognition by disguising or altering appearance. The most effective mitigation is to enhance training data with a wide range of variances in facial features, lighting, and disguises so the system can robustly detect authentic users despite adversarial attempts.
Monitoring and secondary confirmation are supportive controls but are reactive. Fine-tuning to reduce hallucinations is irrelevant in this context, as hallucinations apply more to generative AI. The best preventive measure is strengthening the model with diverse, variance-rich training data.
References:
AAISM Study Guide - AI Technologies and Controls (Robust Training Data Strategies) ISACA AI Security Management - Biometric AI Security Risks
質問 # 61
An organization implementing an LLM application sees unexpected cost increases due to excessive computational resource usage. Which vulnerability is MOST likely in need of mitigation?
- A. Excessive agency
- B. Unbounded consumption
- C. System prompt leakage
- D. Sensitive information disclosure
正解：B
解説：
AAISM categorizes unbounded consumption (also known as "resource exhaustion" or "infinite queries") as an AI-specific vulnerability where attackers (or faulty prompts) trigger excessive computation, leading to high costs and degraded service.
This aligns precisely with unexpected large compute bills.
Excessive agency (A) refers to unsafe autonomy, while disclosure (B) and prompt leakage (D) do not relate to compute overuse.
References: AAISM Study Guide - AI Abuse and Unbounded Consumption Risk.
質問 # 62
Which of the following would BEST protect trade secrets related to AI technologies during their life cycle?
- A. Introducing watermarks when generating AI output
- B. Patenting AI algorithms along with data sets
- C. Restricting access to sensitive data
- D. Enforcing trademark rights in AI systems
正解：C
解説：
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.
質問 # 63
An organization is planning to commission a third-party AI system to make decisions using sensitive data.
Which of the following metrics is MOST important for the organization to consider?
- A. Service availability
- B. Model response time
- C. Accuracy thresholds
- D. Accessibility rating
正解：C
解説：
When AI systems make consequential decisions over sensitive data, AAISM requires explicit performance thresholds tied to decision quality-i.e., accuracy (and related error/false-rate limits) aligned to business risk appetite and regulatory expectations. Availability and latency are important service metrics, but decision integrity and error bounds are primary risk drivers in sensitive contexts. Establishing, monitoring, and enforcing minimum accuracy thresholds (with subgroup performance checks) is essential to reduce harm, ensure fairness/compliance, and support auditability.
References:* AI Security Management (AAISM) Body of Knowledge: Risk-aligned performance metrics; decision quality thresholds; harm and error-rate governance in sensitive processing.* AI Security Management Study Guide: Metric selection for high-risk AI; accuracy, false positive/negative limits, and acceptance criteria tied to business controls.
質問 # 64
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?
- A. Data normalization
- B. Data collection
- C. Data preparation
- D. Data minimization
正解：C
解説：
According to AAISM's data life-cycle model, data preparation is the phase where raw data is transformed into a model-ready format. The materials describe this phase as including "cleaning, encoding, formatting, feature engineering, and other transformations required for model consumption." This directly matches the scenario where a team formats user information to be readable by an AI model. Data minimization (A) is about reducing data to the minimum necessary for the stated purpose. Data collection (C) focuses on acquiring data from different sources. Data normalization (D) is a specific technique (often a sub-activity within preparation) that adjusts numeric values to a common scale; it is narrower than the broader concept of preparation.
Therefore, the activity described is correctly associated with data preparation, which the AAISM framework clearly positions before training and evaluation.
References: AI Security Management™ (AAISM) Study Guide - AI Data Life Cycle; Data Preparation and Pre-processing.
質問 # 65
......
AAISMリアル有効で正確な問題集257問題と解答が待ってます：https://www.goshiken.com/ISACA/AAISM-mondaishu.html
最新のAAISM問題集でPDF：https://drive.google.com/open?id=1SyB2__nXSnSoAfGei4K5iackUgUG8uB-