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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Exam Format:	Multiple Choice, Scenario-based Questions
Exam Duration:	150 minutes
Passing Score:	450/800
Real Exam Qty:	90
Available Languages:	Spanish, English
Certificate Validity Period:	3 years
Related Certifications:	ISACA Advanced in AI Security Management (AAISM)
Exam Price:	USD $399
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Online remote proctored exam or test center exam
Pre Condition:	Candidates must hold an active CISM or CISSP certification.
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:

A. provide a basis for identification of threats and vulnerabilities
B. align the system components of AI with the business goals of the organization
C. ensure the development of powerful, efficient, and scalable AI systems
D. deploy fast and cost-efficient AI systems for rapidly changing environments

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 # 46
In an AI governance framework, the MOST important reason to conduct periodic model validation is to:

A. reduce licensing costs for commercial models.
B. prevent overfitting on local training data.
C. detect performance drift and security vulnerabilities.
D. increase regulatory acceptance and marketing reach.

Answer: C

Explanation:
Periodic model validation is essential to identify degradation in model performance, drift from expected behavior, and emerging security vulnerabilities over time. Continuous validation helps ensure the AI system remains accurate, reliable, secure, and aligned with operational and governance requirements.

NEW QUESTION # 47
To process customer feedback for business decisions, an organization purchases an AI-powered sentiment analysis service implemented as a black box API. Which of the following represents the GREATEST security risk to the business?

A. The service could be subjected to data poisoning attacks that causes sentiment analysis to be manipulated.
B. The organization's API key for the service could be stolen and used by an unauthorized party.
C. The service may secretly exfiltrate customer feedback data for its own purposes.
D. The API endpoint for the service could be unavailable due to a distributed denial of service (DDoS) attack.

Answer: C

Explanation:
Using a black box API means the organization has limited visibility into how data is handled. If the service exfiltrates customer feedback data, it directly compromises confidentiality and trust, posing the most significant security risk to the business.

NEW QUESTION # 48
Which of the following BEST ensures AI components are validated as part of disaster recovery testing?

A. Monitoring model performance metrics during failover and recovery to assess system stability
B. Running simulated data loss scenarios by erasing test records from the AI system's feature store
C. Simulating denial of service (DoS) attacks against AI APIs to evaluate detection capabilities
D. Disconnecting primary model training clusters to test retraining workflow during extended outages

Answer: A

Explanation:
Business continuity and disaster recovery (BC/DR) exercises for AI must validate that critical AI components (feature stores, model registries, inference services, pipelines) operate within agreed recovery objectives during failover and restoration. Monitoring and evaluating model performance and stability during DR tests provides objective evidence that AI services remain functional, accurate, and reliable under contingency conditions, thereby validating the AI stack end-to-end.
Option A focuses on retraining during outages (a niche scenario) rather than validating service continuity for production inference. Option B is security testing, not BC/DR validation. Option C tests data loss handling but does not comprehensively validate AI service behavior across failover and recovery.
References: AI Security Management™ (AAISM) Body of Knowledge: "Operational Resilience-BC/DR for AI Systems," "Validation and Evidence of Continuity"; AAISM Study Guide: "AI DR Test Planning- Metrics, Model Performance Validation, and Recovery Readiness."

NEW QUESTION # 49
What is the GREATEST benefit of performing AI security risk assessments?

A. Implementing privacy controls
B. Securing appropriate funding
C. Enabling risk prioritization
D. Updating the risk register

Answer: C

Explanation:
AAISM emphasizes that the primary value of AI security risk assessments is prioritizing risks based on likelihood, impact, and business relevance.
Updating the register (A) is administrative. Privacy controls (B) are one category of mitigation. Funding (D) is possible but not the primary purpose.
References: AAISM Study Guide - AI Risk Assessment Objectives; Prioritization and Governance.

NEW QUESTION # 50
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