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Pass Guaranteed Quiz 2026 ISACA Newest AAISM Latest Exam Cost

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Pass Guaranteed Quiz 2026 ISACA Newest AAISM Latest Exam Cost

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ISACA AAISM Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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.

AAISM Test Result | AAISM Questions PdfThis is a simple and portable document of real ISACA AAISM Exam Questions. It contains actual ISACA AAISM exam questions and answers and can be helpful for quick revision or for studying on the go. It is also printable so you can easily study on a hard copy of the pdf having a break from staring.
ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q82-Q87):NEW QUESTION # 82
To ensure ethical and responsible AI use, which AI usage policy metric is MOST important to monitor?
  • A. Frequency of policy consultations by employees
  • B. Frequency of policy reviews and updates
  • C. Number of policy violations
  • D. Number of AI projects reviewed for compliance
Answer: A
Explanation:
AAISM states the most meaningful policy performance metric is how often employees consult AI policies, which reflects:
* awareness
* practical adoption
* reliance on policy guidance
* safe decision-making behavior
Violations (A) are lagging indicators. Compliance reviews (B) measure oversight, not behavior. Policy review frequency (D) tracks governance updates, not usage.
References: AAISM Study Guide - AI Policy Effectiveness Metrics.

NEW QUESTION # 83
The PRIMARY benefit of implementing moderation controls in generative AI applications is that it can:
  • A. Optimize the model's response time
  • B. Filter out harmful or inappropriate content
  • C. Increase the model's ability to generate diverse and creative content
  • D. Ensure the generated content adheres to privacy regulations
Answer: B
Explanation:
AAISM materials identify the primary benefit of moderation controls in generative AI systems as their ability to filter out harmful, offensive, or inappropriate content before it is delivered to users. This safeguards organizational reputation, compliance, and user trust. While moderation may indirectly support compliance with privacy requirements, its main function is ensuring that outputs align with ethical and safety standards.
Moderation does not enhance creativity or response speed. Its primary value is in controlling the quality of generated outputs by blocking harmful content.
References:
AAISM Study Guide - AI Technologies and Controls (Moderation and Output Controls) ISACA AI Security Management - Harmful Content Mitigation in Generative AI

NEW QUESTION # 84
Which of the following is the MOST critical success factor for an AI implementation project?
  • A. Ensuring AI risk is captured in the risk register
  • B. Obtaining senior management buy-in
  • C. Mapping data throughout the life cycle
  • D. Developing and using model cards
Answer: B
Explanation:
AAISM identifies executive sponsorship and senior management buy-in as the foremost success factor for AI initiatives. It secures resources, resolves cross-functional conflicts, sets risk appetite, and enforces adherence to governance and controls. Model cards (A), risk registers (B), and lifecycle data mapping (C) are vital practices within the program, but without top-level commitment, adoption, funding, and accountability often fail.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Program Governance; Executive Sponsorship & Accountability; Strategy-to-Control Alignment for Successful AI Delivery.

NEW QUESTION # 85
Which of the following is the MOST important consideration for an organization that has decided to adopt AI to leverage its competitive advantage?
  • A. Develop a comprehensive strategic roadmap for AI integration
  • B. Develop a comprehensive risk management process to address AI-related issues
  • C. Develop internal training programs on AI governance, risk, and compliance (GRC)
  • D. Develop a business case for the procurement of AI monitoring tools
Answer: A
Explanation:
AAISM's governance guidance emphasizes that adopting AI for competitive advantage must begin with a comprehensive strategic roadmap for integration. This roadmap aligns AI adoption with business objectives, sets priorities, defines milestones, and ensures coordination across functions. Risk management, training, and tool procurement are essential, but they are tactical steps that follow once the strategic direction is defined.
Without a roadmap, adoption becomes fragmented and risks misalignment with business strategy. The most important consideration at the adoption stage is therefore creating a strategic integration roadmap.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Strategy and Roadmapping) AI Security Management Study Guide - Business Alignment of AI Initiatives

NEW QUESTION # 86
Which of the following datasets is used to tune hyperparameters?
  • A. Configuration
  • B. Validation
  • C. Training
  • D. Test
Answer: B
Explanation:
Per AAISM's ML lifecycle controls, hyperparameter tuning is performed on the validation set, reserving the test set strictly for final, unbiased performance estimation. The training set is used to fit parameters; the validation set guides model selection and hyperparameter optimization; the test set is untouched until the end to prevent leakage and optimistic bias. "Configuration" is not a dataset type in the lifecycle split.
References:* AI Security Management (AAISM) Body of Knowledge: Model Development Controls- Data Splitting and Evaluation Integrity* AAISM Study Guide: Overfitting Avoidance; Validation vs. Test Separation; Leakage Prevention* AAISM Mapping to Standards: Evaluation Integrity-Hold-out Protocols and Tuning Practices

NEW QUESTION # 87
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