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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Available Languages:Spanish, English
Exam Price:USD $399
Related Certifications:ISACA Advanced in AI Security Management (AAISM)
Certificate Validity Period:3 years
Exam Format:Scenario-based Questions, Multiple Choice
Passing Score:450/800
Exam Duration:150 minutes
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 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 2
  • 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 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q312-Q317):

NEW QUESTION # 312
An organization is facing a deepfake attack intended to manipulate stock prices. The organization's crisis communication plan has been activated. Which of the following is MOST important to include in the initial response?

Answer: D

Explanation:
AAISM guidance on crisis management and communication emphasizes that the initial priority in responding to a reputational or market manipulation attack is to provide accurate clarifying information to the public through a pre-approved statement. This ensures stakeholders and markets are given verified facts immediately, limiting the spread of misinformation. While forensic analysis, employee training, and monitoring activities are important, they occur after the immediate need for public trust and damage control is addressed. Pre-approved statements are a central control in AI-related incident response to ensure consistency, timeliness, and credibility in communications.
References:
AAISM Study Guide - AI Governance and Program Management (Incident Response and Crisis Communication) ISACA AI Security Management - Public Communication and Trust Preservation


NEW QUESTION # 313
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?

Answer: A

Explanation:
According to AAISM's data life-cycle model, data preparation is the phase where raw data is transformed into a model-ready format. The materials describe this phase as including "cleaning, encoding, formatting, feature engineering, and other transformations required for model consumption." This directly matches the scenario where a team formats user information to be readable by an AI model. Data minimization (A) is about reducing data to the minimum necessary for the stated purpose. Data collection (C) focuses on acquiring data from different sources. Data normalization (D) is a specific technique (often a sub-activity within preparation) that adjusts numeric values to a common scale; it is narrower than the broader concept of preparation.
Therefore, the activity described is correctly associated with data preparation, which the AAISM framework clearly positions before training and evaluation.
References: AI Security Management™ (AAISM) Study Guide - AI Data Life Cycle; Data Preparation and Pre-processing.


NEW QUESTION # 314
Which of the following BEST describes the role of model cards in AI solutions?

Answer: C

Explanation:
AAISM positions model cards as standardized documentation artifacts that record intended use and out-of- scope use, training/evaluation data characteristics, performance metrics across groups, limitations/risks
, and governance controls/owners. Their purpose is transparency and assurance, not automated tuning or synthetic data generation. Visualization (A) may appear within a card, but the core role is structured documentation for governance, risk, and compliance.
References: AI Security Management (AAISM) Body of Knowledge - Documentation & Transparency Artifacts; Model Cards for Governance, Risk, and Assurance; Intended Use, Limitations, and Performance Disclosure.


NEW QUESTION # 315
When addressing privacy concerns related to AI, what is the GREATEST significance of user consent?

Answer: B

Explanation:
AAISM clarifies that the primary regulatory purpose of user consent is to provide lawful authorization for processing personal data, including use in AI models.
Consent does not directly prevent unauthorized access (A). Deletion rights (B) relate to data subject rights but are not the purpose of consent. Bias detection (D) is unrelated.
References: AAISM Study Guide - AI Privacy and Legal Basis for Processing Data.


NEW QUESTION # 316
A programmer suspects an AI system is inferring sensitive user information. What is the BEST action?

Answer: A

Explanation:
AAISM directs that potential privacy, ethical, or compliance risks must be escalated to the AI Governance Panel, the body responsible for oversight, risk approval, and corrective action.


NEW QUESTION # 317
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