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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 (Q279-Q284):

NEW QUESTION # 279
An organization is reviewing an AI application to determine whether it is still needed. Engineers have been asked to analyze the number of incorrect predictions against the total number of predictions made. Which of the following is this an example of?

Answer: C

Explanation:
AAISM guidance identifies metrics like error rate versus total predictions as a key performance indicator (KPI) for evaluating AI model effectiveness. KPIs provide measurable values to assess performance against objectives. Model validation is broader and occurs prior to production use, testing the model against predefined standards. Control self-assessment relates to governance processes, not predictive accuracy. Explainable decision-making refers to interpretability, not error-rate evaluation. Thus, analyzing incorrect predictions against total predictions is a performance measure, making it a KPI.


NEW QUESTION # 280
A newly hired programmer suspects that the organization's AI solution is inferring users' sensitive information and using it to advise future decisions. Which of the following is the programmer's BEST course of action?

Answer: D

Explanation:
AAISM directs personnel to use established AI governance channels for suspected privacy, ethics, or compliance risks. The governance panel (risk, privacy, legal/compliance, security, product/data science) is chartered to triage, record, investigate, and direct remediation for potential inference of sensitive attributes and resulting decision impacts. Direct technical action (A or C) bypasses due process and accountability; escalating directly to a single executive (B) lacks the structured, cross-functional oversight required for regulated and ethical AI risk handling.


NEW QUESTION # 281
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:

Answer: D

Explanation:
AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.
References:
AAISM Study Guide - AI Risk Management (Bias and Ethics Validation)
ISACA AI Security Management - Ethical AI Practices


NEW QUESTION # 282
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: C

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 # 283
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?

Answer: D

Explanation:
Under AAISM governance, management and control owners are accountable for remediation. For AI systems, the accountable role is the Model Owner (or equivalent business/service owner), who coordinates with architects, engineers, and operations to implement corrective actions and report closure. Internal auditors provide independent assurance and do not implement fixes; end users are not remediation owners.
References: AI Security Management (AAISM) Body of Knowledge - Roles & Accountability (Model Owner, Control Owner, Assurance); Audit Findings Management and Remediation Governance.


NEW QUESTION # 284
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