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[General] Exam AAISM Objectives Pdf, Test AAISM Guide Online

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【General】 Exam AAISM Objectives Pdf, Test AAISM Guide Online

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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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q166-Q171):NEW QUESTION # 166
The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:
  • A. deploy fast and cost-efficient AI systems for rapidly changing environments
  • B. align the system components of AI with the business goals of the organization
  • C. provide a basis for identification of threats and vulnerabilities
  • D. ensure the development of powerful, efficient, and scalable AI systems
Answer: B
Explanation:
An AI architecture, within program governance, exists to align AI system components and lifecycle processes with business goals and policy constraints. Architecture provides the organizing structure linking strategy, capabilities, processes, data, models, controls, and assurance so that AI outcomes are traceable to business value, risk appetite, and compliance expectations. Efficiency, speed, and threat analysis are important architectural qualities, but they are not the primary purpose; the primary purpose is strategic and governance alignment so that technical choices and controls consistently realize organizational objectives.
References:* AI Security Management (AAISM) Body of Knowledge: AI Program Architecture - alignment of capabilities, processes, and controls to business objectives* AI Security Management Study Guide: Architecture-driven governance, traceability from business goals to technical and control design

NEW QUESTION # 167
Which of the following BEST ensures AI components are validated during disaster recovery testing?
  • A. Running simulated data-loss scenarios by deleting test feature-store records
  • B. Disconnecting model training clusters to test retraining workflows
  • C. Simulating DoS attacks on AI APIs
  • D. Monitoring model performance during failover and recovery
Answer: D
Explanation:
AAISM states that AI disaster recovery testing must validate that models behave correctly during failover.
The only option that tests actual operational continuity of AI components is:
# monitoring model performance during failover
This validates stability, functionality, and resilience under disaster conditions.
Options A, B, and C test isolated scenarios but do not validate end-to-end AI operational continuity.
References: AAISM Study Guide - AI Resilience & Disaster Recovery Testing.

NEW QUESTION # 168
An organization is designing an AI-based credit risk assessment system integrating sensitive financial data.
Which option BEST supports security-by-design?
  • A. Restricting access to AI models using IP allow lists
  • B. Segmenting AI services across containers
  • C. Integrating differential privacy mechanisms into model training
  • D. Applying threat modeling specific to AI components before deployment
Answer: D
Explanation:
AAISM identifies AI-specific threat modeling as an essential early-stage control in security-by-design, particularly for high-risk systems like credit scoring. It systematically identifies:
* data poisoning
* bias vulnerabilities
* model evasion
* model extraction
* misuse scenarios
Differential privacy (A) is powerful but is a mitigation, not the overarching design control. Segmentation (C) and IP allow lists (D) are supporting controls but not the foundational step in secure design.
References: AAISM Study Guide - Security-by-Design; AI Threat Modeling.

NEW QUESTION # 169
Which of the following AI system vulnerabilities is MOST easily exploited by adversaries?
  • A. Weak controls for access to the AI model
  • B. Inaccurate generalizations from new data by the AI model
  • C. Lack of protection against denial of service (DoS) attacks
  • D. Inability to detect input modifications causing inappropriate AI outputs
Answer: A
Explanation:
AAISM study materials stress that weak access controls are the most easily exploited vulnerability in AI systems. Without strong access restrictions, adversaries can directly query, extract, manipulate, or overload models, leading to data leakage or compromised outputs. While inaccurate generalizations, DoS vulnerabilities, or susceptibility to input manipulation are serious, they typically require more effort or specific conditions. Weak access control provides the most direct and immediate entry point for attackers. As such, it is identified as the most easily exploited vulnerability.
References:
AAISM Exam Content Outline - AI Risk Management (Access and Authentication Vulnerabilities) AI Security Management Study Guide - Exploitable Weaknesses in AI Systems

NEW QUESTION # 170
Which of the following key risk indicators (KRIs) is MOST relevant when evaluating the effectiveness of an organization's AI risk management program?
  • A. Number of AI-related training requests submitted
  • B. Percentage of AI projects in compliance
  • C. Number of AI models deployed into production
  • D. Percentage of critical business systems with AI components
Answer: B
Explanation:
AAISM identifies percentage of AI projects in compliance as the most relevant KRI for evaluating AI risk management effectiveness. This metric directly reflects adherence to governance, regulatory, and security requirements. The number of models deployed (A) or systems with AI components (B) indicate scale, not risk management quality. Training requests (D) show awareness levels but do not measure effectiveness of risk management. Compliance percentage provides a direct, measurable indication of how well risks are being governed and mitigated.
References:
AAISM Exam Content Outline - AI Risk Management (Risk Metrics and Compliance) AI Security Management Study Guide - Key Risk Indicators in AI Programs

NEW QUESTION # 171
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The ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) exam questions can help you gain the high-in-demand skills and credentials you need to pursue a rewarding career. To do this you just need to pass the ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) certification exam which is not easy to crack. You have to put in some extra effort, and time and prepare thoroughly to pass the ISACA AAISM Exam. For the quick, complete, and comprehensive ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) exam dumps preparation you can get help from top-notch and easy-to-use AAISM Questions.
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