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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Passing Score:450/800
Exam Price:USD $399
Exam Duration:150 minutes
Exam Format:Multiple Choice, Scenario-based Questions
Certificate Validity Period:3 years
Available Languages:English, Spanish
Related Certifications:ISACA Advanced in AI Security Management (AAISM)
Real Exam Qty:90
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:

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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q343-Q348):

NEW QUESTION # 343
When evaluating a new AI tool for intrusion prevention, which of the following is the MOST important consideration to ensure the tool fits within the existing program architecture?

Answer: D

Explanation:
The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization' s established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance. While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to-and can be verified against-the control objectives that define the program's intended outcomes.
References:* AI Security Management™ (AAISM) Body of Knowledge: AI Governance and Program Management - Security program alignment, control objectives, and requirements traceability* AI Security Management™ Study Guide: Control objective mapping, architecture fit criteria, and solution selection governance


NEW QUESTION # 344
A hospital adopts an AI solution from an external vendor to help diagnose rare diseases. Which of the following BEST demonstrates the verification of security requirements for this technology?

Answer: C

Explanation:
Verification of security requirements for an AI used in diagnosis must focus on how sensitive health data (PHI) is protected in line with regulations such as HIPAA and similar healthcare laws.
Integrating explicit requirements for handling, storing, transmitting, and accessing sensitive data in accordance with healthcare regulations is a direct verification that the technology's security controls meet the hospital's legal and security obligations.


NEW QUESTION # 345
Which of the following should be the MOST important consideration when conducting an AI impact assessment?

Answer: A

Explanation:
AAISM defines an AI Impact Assessment (AIIA) as a structured evaluation designed to determine whether an AI system can safely and responsibly support business objectives without creating unacceptable risks.
The framework states that alignment to business objectives is the central purpose of AI adoption and therefore must be the starting point in an impact assessment.


NEW QUESTION # 346
The extent of stakeholder engagement in generative AI policy development PRIMARILY depends on the:

Answer: B

Explanation:
The level of stakeholder engagement should be driven by how significantly AI use can affect the organization, including risks, ethical considerations, and business outcomes. Greater potential impact requires broader and deeper stakeholder involvement to ensure appropriate governance and risk management.


NEW QUESTION # 347
An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data.
Which of the following is the MOST effective data cleaning technique to improve model performance?

Answer: D

Explanation:
The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction.
Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.
References:
AAISM Study Guide - AI Risk Management (Data Integrity and Quality Controls) ISACA AI Governance Guidance - Data Preparation and Bias Mitigation


NEW QUESTION # 348
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