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[General] Valid Braindumps AAISM Free | AAISM Reliable Test Sims

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【General】 Valid Braindumps AAISM Free | AAISM Reliable Test Sims

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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 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 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.

Types of AAISM Exam Practice Test QuestionsPracticing with ISACA AAISM Exam questions will help you to become an expert in and acquire the ISACA AAISM. ISACA AAISM Exam Questions allow you to verify your skills as a professional. You have to pass the ISACA AAISM to achieve the associate-level certification.
ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q27-Q32):NEW QUESTION # 27
Which of the following is the MOST effective strategy for penetration testers assessing the security of an AI model against membership inference attacks?
  • A. Generating synthetic data to replace the training data
  • B. Disabling AI model logging to reduce noise during testing
  • C. Measuring AI model accuracy on the test set
  • D. Analyzing AI model confidence scores to indicate training data
Answer: D
Explanation:
AAISM identifies confidence-score analysis as a principal technique for evaluating exposure to membership inference: models often yield measurably higher confidence for points seen during training. Testers compare output probabilities/entropies for known in-training vs. out-of-training samples to assess leakage. Disabling logs (A) reduces evidence; test-set accuracy (B) does not measure privacy leakage; synthetic data generation (D) is a mitigation strategy, not a penetration-testing method.
References: AI Security Management™ (AAISM) Body of Knowledge - Model Privacy Threats:
Membership Inference; Red/Blue Team Evaluation Techniques; Confidence/Entropy-based Privacy Testing.

NEW QUESTION # 28
An organization decides to contract a vendor to implement a new set of AI libraries. Which of the following is MOST important to address in the master service agreement to protect data used during the AI training process?
  • A. Independent certification
  • B. Continuous data monitoring
  • C. Data pseudonymization
  • D. Right to audit
Answer: D
Explanation:
AAISM emphasizes that the right to audit is the most critical contractual safeguard when outsourcing AI services. This allows the contracting organization to independently verify that the vendor is applying appropriate protections to training data, meeting compliance obligations, and upholding privacy requirements.
Pseudonymization is a technical method, monitoring is operational, and certifications provide external assurance, but none give the direct, enforceable oversight that audit rights provide. In vendor contracts, the right to audit is the primary safeguard for data protection and governance.
References:
AAISM Study Guide - AI Governance and Program Management (Third-Party Contracts and Audit Rights) ISACA AI Security Management - Vendor Governance Controls

NEW QUESTION # 29
Cybersecurity teams should FIRST be embedded in the:
  • A. Model deployment phase
  • B. Model training phase
  • C. Model testing phase
  • D. Model design phase
Answer: D
Explanation:
AAISM stresses that security must be embedded from the earliest phase-design, ensuring:
* threat modeling
* secure architecture
* data protection requirements
* system boundaries
* security-by-design principles
Introducing cybersecurity later increases unmitigated vulnerabilities.
Testing (A), training (C), and deployment (B) occur after foundational security decisions have already been made.
References: AAISM Study Guide - Secure AI Lifecycle; Early Security Involvement.

NEW QUESTION # 30
Within an incident handling process, which of the following would BEST help restore end-user trust in an AI system?
  • A. The AI model prioritizes incidents based on business impact
  • B. The AI model's outputs are validated by team members
  • C. AI is used to monitor incident detection and alerts
  • D. Remediation of the AI system based on lessons learned
Answer: D
Explanation:
AAISM highlights that post-incident remediation and demonstrating lessons learned is essential to restoring trust. Governance guidance specifies that stakeholders regain confidence only when organizations show clear corrective actions, transparency, and improvements to prevent recurrence.
Validating outputs (B) supports accuracy but is not trust-restoring. Monitoring (C) and prioritization (D) relate to operations, not trust rebuilding.
References: AAISM Study Guide - AI Governance; Incident Response and Trust Restoration.

NEW QUESTION # 31
An organization utilizes AI-enabled mapping software to plan routes for delivery drivers. A driver following the AI route drives the wrong way down a one-way street, despite numerous signs. Which of the following biases does this scenario demonstrate?
  • A. Reporting
  • B. Confirmation
  • C. Selection
  • D. Automation
Answer: D
Explanation:
AAISM defines automation bias as the tendency of individuals to over-rely on AI-generated outputs even when contradictory real-world evidence is available. In this scenario, the driver ignores traffic signs and follows the AI's instructions, showing blind reliance on automation. Selection bias relates to data sampling, reporting bias refers to misrepresentation of results, and confirmation bias involves interpreting information to fit pre-existing beliefs. The most accurate description is automation bias.
References:
AAISM Exam Content Outline - AI Risk Management (Bias Types in AI)
AI Security Management Study Guide - Automation Bias in AI Use

NEW QUESTION # 32
......
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