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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Exam Price:	USD 459 (member) / USD 599 (non-member)
Exam Format:	Multiple-choice questions
Exam Duration:	150 minutes
Available Languages:	Spanish, English
Real Exam Qty:	90
Certificate Validity Period:	3 years (continuing professional education required for renewal)
Passing Score:	450/800
Related Certifications:	CISSP CISM
Recommended Training:	ISACA Official Training & Review Materials
Exam Registration:	ISACA AAISM Credentialing Page
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Computer-based exam (online proctored or authorized test center)
Pre Condition:	Recommended: Active CISM or CISSP certification and experience in IT/security management or advisory roles
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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 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 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 (Q281-Q286):

NEW QUESTION # 281
Which of the following is the MOST important course of action when implementing continuous monitoring and reporting for AI-based systems?

A. Implement a risk dashboard for visualizing and tracking AI-related risk over time
B. Implement real-time monitoring of key risk indicators (KRIs) for AI systems
C. Develop standardized risk reporting templates for different stakeholder groups
D. Establish an automated alert system for threshold breaches in risk metrics

Answer: B

Explanation:
The AAISM governance framework specifies that the foundation of continuous monitoring is real- time tracking of key risk indicators. This ensures immediate detection of deviations, model drift, and operational anomalies. Automated alerts, dashboards, and reporting templates all support monitoring, but they rely on the presence of accurate, real-time KRI measurement as their source. Without live monitoring, the other controls are reactive rather than proactive. The most important course of action in establishing effective continuous monitoring is therefore real-time KRI tracking.

NEW QUESTION # 282
Which of the following is the MOST critical key risk indicator (KRI) for an AI system?

A. The response time of the model
B. The rate of drift in the model
C. The accuracy rate of the model
D. The amount of data in the model

Answer: B

Explanation:
AAISM highlights that while accuracy and performance metrics are important, the rate of drift is the most critical KRI for AI systems. Model drift occurs when input data or environmental conditions shift, causing the system to degrade and produce unreliable outputs. This risk indicator directly reflects whether the AI continues to function as intended over time. Accuracy rates and response times are performance metrics, not primary risk signals. The amount of data in the model does not reliably indicate exposure to risk. Therefore, the greatest KRI for ongoing assurance and governance is the rate of drift.
References:
AAISM Study Guide - AI Risk Management (Monitoring and Drift Detection) ISACA AI Security Management - Key Risk Indicators for AI Systems

NEW QUESTION # 283
Which of the following attack types is the MOST likely cause of model drift?

A. Model stealing
B. Data poisoning
C. Membership inference
D. Perfect knowledge

Answer: B

Explanation:
AAISM defines data poisoning as directly capable of causing model drift because corrupted training data shifts the statistical distribution, leading to degraded or unsafe performance.

NEW QUESTION # 284
Which of the following is the MOST important course of action prior to placing an in-house developed AI solution into production?

A. Deploy a prototype of the solution
B. Perform testing, evaluation, validation, and verification
C. Perform a privacy, security, and compliance gap analysis
D. Obtain senior management sign-off

Answer: B

Explanation:
AAISM lifecycle governance guidance specifies that before any AI solution is moved into production, it must undergo testing, evaluation, validation, and verification to ensure accuracy, resilience, security, and compliance with standards. These steps confirm that the solution performs as expected under varied conditions. Conducting gap analysis is part of compliance checks but comes earlier in design. Management sign-off provides approval but cannot substitute for assurance of technical reliability. Deploying prototypes is a testing method but not the final assurance step. The critical requirement is a complete cycle of testing, validation, and verification.

NEW QUESTION # 285
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. Monitoring model performance during failover and recovery
C. Simulating DoS attacks on AI APIs
D. Disconnecting model training clusters to test retraining workflows

Answer: B

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 # 286
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