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[General] AAISM Exam Preparation & AAISM Passed

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【General】 AAISM Exam Preparation & AAISM Passed

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ISACA AAISM Exam Syllabus Topics:
TopicDetails
Topic 1
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 3
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q112-Q117):NEW QUESTION # 112
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?
  • A. End users
  • B. Model owners
  • C. Internal auditors
  • D. System architects
Answer: B
Explanation:
AAISM clarifies that model owners hold responsibility for ensuring corrective actions are implemented after AI audits. They are accountable for:
* model behavior
* compliance gaps
* security improvements
* governance alignment
Internal auditors (B) perform assessments but do not implement changes. System architects (A) support technical fixes but do not own compliance. End users (C) are not responsible for audit remediation.
References: AAISM Study Guide - Roles & Responsibilities; AI Ownership and Accountability.

NEW QUESTION # 113
Which of the following technologies can be used to manage deepfake risk?
  • A. Adaptive authentication
  • B. Systematic data tagging
  • C. Multi-factor authentication (MFA)
  • D. Blockchain
Answer: D
Explanation:
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

NEW QUESTION # 114
An organization decides to use an anomaly-based intrusion detection system (IDS) integrated with a generative adversarial network-enabled AI tool. The integrated tool would MOST effectively detect intrusions by leveraging:
  • A. synthetic intrusion data to train the tool's components
  • B. validation data sets to enable highly realistic AI decisions
  • C. classified real intrusion data based on labeled data
  • D. automated rule creation to increase model performance
Answer: A
Explanation:
AAISM describes GANs as effective for synthetic data generation to augment scarce or imbalanced security datasets. In anomaly IDS contexts, GANs can create realistic synthetic attack traffic and edge-case behaviors that improve detector sensitivity and robustness. While labeled "real" data is valuable, the specific advantage of a GAN-integrated pipeline is the capability to generate adversarially realistic synthetic intrusions for training and stress testing. Automated rules are a signature-based paradigm and do not leverage GAN strengths; validation sets are for evaluation, not primary improvement of anomaly coverage.
References:* AI Security Management™ (AAISM) Body of Knowledge: Security data engineering; synthetic data via generative models for rare-event detection; adversarial augmentation for IDS.* AI Security Management™ Study Guide: Model robustness with synthetic adversarial examples; training-set enrichment for anomaly detection.

NEW QUESTION # 115
Which of the following BEST enables an organization to maintain visibility to its AI usage?
  • A. Measuring the impact of AI implementation using key performance indicators (KPIs)
  • B. Maintaining a comprehensive inventory of AI systems and business units that leverage them
  • C. Ensuring the board approves the policies and standards that define corporate AI strategy
  • D. Maintaining a monthly dashboard that captures all AI vendors
Answer: B
Explanation:
The AAISM framework stresses that the most effective way to maintain oversight of organizational AI usage is by maintaining a comprehensive inventory of all AI systems and the business units using them. Such an inventory provides a centralized, transparent record of where AI is deployed, ensuring accountability, monitoring, and compliance. While board approval, dashboards, and KPIs are important governance tools, they do not provide holistic visibility across the enterprise. The inventory ensures traceability and governance alignment, making it the best method to maintain visibility of AI usage.
References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories) ISACA AI Security Management - Centralized Oversight of AI Assets

NEW QUESTION # 116
Which of the following is MOST important to consider when validating a third-party AI tool?
  • A. Right to audit
  • B. Roundtable testing
  • C. Terms and conditions
  • D. Industry analysis and certifications
Answer: A
Explanation:
The AAISM framework specifies that when adopting third-party AI tools, the right to audit is the most critical contractual and governance safeguard. This ensures that the organization can independently verify compliance with security, privacy, and ethical requirements throughout the lifecycle of the tool. Terms and conditions provide general usage guidance but often limit liability rather than ensuring transparency. Industry certifications may indicate good practice but do not substitute for direct verification. Roundtable testing is useful for evaluation but lacks enforceability. Only the contractual right to audit provides formal assurance that the tool operates in accordance with organizational policies and external regulations.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Third-Party Governance) AI Security Management Study Guide - Vendor Oversight and Audit Rights

NEW QUESTION # 117
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