AAISM最新資料 &優秀な日本語復習赤本確実にISACA ISACA Advanced in AI Security Management (AAISM) Examをクリアするのに役立ちます

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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Risk Management31%- AI Risk Assessment
  • 1. Risk thresholds and treatment
    • 2. Risk identification and analysis
      - AI Threats and Vulnerabilities
      • 1. Adversarial AI and ML attacks
        • 2. Data poisoning and model manipulation
          • 3. Supply chain and vendor risks
            Topic 2: AI Technologies and Controls38%- AI Assurance and Safety
            • 1. Bias mitigation
              • 2. Ethical AI controls
                • 3. Model validation and testing
                  - AI Security Architecture
                  • 1. Model lifecycle security
                    • 2. Secure AI system design
                      - Data and Model Controls
                      • 1. Data protection and governance
                        • 2. Privacy-preserving techniques
                          • 3. Monitoring and runtime controls
                            Topic 3: AI Governance and Program Management31%- AI Strategy and Policy
                            • 1. Responsible and acceptable use
                              • 2. AI strategy development
                                • 3. Policies and procedures
                                  - AI Governance Foundations
                                  • 1. Roles and responsibilities
                                    • 2. Organizational structure and governance
                                      • 3. Stakeholder considerations
                                        - Frameworks and Compliance
                                        • 1. Regulatory requirements
                                          • 2. Privacy and ethics considerations
                                            • 3. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001)

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                                              ISACA Advanced in AI Security Management (AAISM) Exam 認定 AAISM 試験問題 (Q235-Q240):

                                              質問 # 235
                                              Implementing which of the following would MOST effectively address bias in generative AI models?

                                              正解：D

                                              解説：
                                              AAISM identifies fairness constraints (e.g., constrained optimization, debiasing objectives, conditional generation controls, and post-processing calibrations) as the most direct, measurable method to mitigate disparate outcomes in generative systems. While data augmentation can help with coverage, and adversarial training improves robustness, fairness constraints explicitly target distributional fairness and outcome equity in generated content, aligning with governance and compliance goals.
                                              References: AI Security Management (AAISM) Body of Knowledge - Fairness & Bias Management in Generative AI; Metrics, Constraints, and Remediation. AAISM Study Guide - Fairness Objectives, Post-hoc Debiasing, and Evaluation Protocols.


                                              質問 # 236
                                              Which of the following control strategies BEST mitigates the risk of user harm from a large language model (LLM) generating offensive or biased content?

                                              正解：C

                                              解説：
                                              Output filtering and moderation directly address the risk of harmful, offensive, or biased content by reviewing and controlling generated responses before they reach users. This helps enforce safety, ethical, and compliance standards in LLM outputs.


                                              質問 # 237
                                              A vendor switched its chatbot's AI model without due diligence, causing unethical investment advice. What control BEST prevents this scenario?

                                              正解：A

                                              解説：
                                              AAISM stresses AI-specific change management as essential for vendor-driven or system-driven updates.
                                              Proper change control includes:
                                              * impact assessments
                                              * ethical review
                                              * risk evaluation
                                              * approval checkpoints
                                              * rollback plans
                                              An MSA (A) supports contracts but does not manage operational change. Shared responsibility (C) describes roles, not change control. Data minimization (D) reduces exposure but doesn't control model updates.
                                              References: AAISM Study Guide - AI Change Management; Vendor Model Update Controls.


                                              質問 # 238
                                              When deriving statistical information generated by AI systems, which of the following types of risk is MOST important to address?

                                              正解：D

                                              解説：
                                              The most critical risk when deriving statistical insights from AI-generated data is systemic bias in data. According to the AI Security ManagementTM (AAISM) framework, systemic bias directly undermines the fairness, reliability, and validity of analytical results derived from AI systems. If the input data or learned model patterns are biased--reflecting skewed representation, sampling imbalance, or embedded prejudice--the statistical outputs will propagate and amplify these biases, leading to misinformed decisions and compliance violations.


                                              質問 # 239
                                              When deriving statistical information generated by AI systems, which of the following types of risk is MOST important to address?

                                              正解：D

                                              解説：
                                              The most critical risk when deriving statistical insights from AI-generated data is systemic bias in data.
                                              According to the AI Security Management™ (AAISM) framework, systemic bias directly undermines the fairness, reliability, and validity of analytical results derived from AI systems. If the input data or learned model patterns are biased-reflecting skewed representation, sampling imbalance, or embedded prejudice- the statistical outputs will propagate and amplify these biases, leading to misinformed decisions and compliance violations.
                                              Why Option A is Correct:
                                              * Systemic bias affects the integrity and trustworthiness of AI-generated statistical information.
                                              * It can introduce discriminatory outcomes, ethical breaches, and regulatory non-compliance-key concerns in AAISM's AI Risk Management and Governance principles.
                                              * Mitigating systemic bias requires data quality assessments, fairness audits, bias detection tools, and model interpretability measures to ensure the derived insights are accurate and ethically sound.
                                              Why Other Options Are Incorrect:
                                              * Option B: Incomplete outputs can affect accuracy but are typically handled through process monitoring or retraining, not as a primary risk factor in statistical validity.
                                              * Option C: Lack of data normalization is a technical preprocessing issue, not a governance-level risk impacting statistical trustworthiness.
                                              * Option D: Hallucinations occur mainly in generative models (e.g., LLMs) and affect content generation, not statistical computation pipelines.
                                              Exact Extract from Official AAISM Study Guide:
                                              "Systemic bias in AI training and inference data represents the most material statistical risk. Bias propagates through derived metrics, predictive models, and decision outputs, compromising fairness, accuracy, and compliance. AI Security Management requires implementing bias detection, fairness testing, and governance mechanisms to identify and mitigate such systemic bias before using AI-generated analytics for organizational or regulatory reporting." References:
                                              AI Security Management™ (AAISM) Body of Knowledge: AI Risk Identification and Evaluation, Bias and Fairness Management in AI Systems.
                                              AI Security Management™ Study Guide: Systemic Bias Mitigation Techniques, Fairness Assurance in AI Analytics.
                                              ISO/IEC 23894:2023 - Clause 7.2: Bias identification and treatment within AI risk frameworks.


                                              質問 # 240
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