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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 (Q280-Q285):

NEW QUESTION # 280
Which of the following is the MOST effective use of AI-enabled tools in a security operations center (SOC)?

Answer: D

Explanation:
Themost effective SOC applicationof AI is indetecting subtle, hard-to-find attack patternsthat reduce false negatives.
AAISM technical control guidance notes that AI in SOCs is best applied to:
* Enhance detection accuracy and sensitivity to anomalies.
* Assist analysts in identifying hidden patterns that traditional rule-based systems miss.
* Augment-not replace-human decision-making for high-confidence outcomes.
Options B and C incorrectly shift responsibility entirely to AI, which contradicts governance principles requiringhuman oversight. Option D is useful for efficiency, but theprimary effectivenesscomes from improving detection quality.
Therefore, the most effective use is toreduce false negatives and detect subtle attacks.


NEW QUESTION # 281
When creating a use case for an AI model that provides sensitive decisions affecting end users, which of the following is the GREATEST benefit of using model cards?

Answer: D

Explanation:
AAISM highlights that model cards are a governance tool designed to document ethical considerations, limitations, fairness constraints, data sources, and suitability of use cases for AI models--especially when they affect individuals' rights, opportunities, or access to services.
Their greatest value is providing transparency and ethical clarity, ensuring stakeholders understand risks, bias considerations, and how decisions impact users.


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: D

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
Which of the following actions BEST enables the evaluation of bias during an AI impact assessment?

Answer: D

Explanation:
In AI Security Management, bias evaluation is primarily a data problem before it is a model or performance problem. The official AI Security Management content explains that impact assessments must specifically analyze training data representativeness against the demographics and characteristics of all relevant users and stakeholders. If the data used to train an AI system underrepresents or omits particular groups, the resulting model will systematically disadvantage them, regardless of how fast or "accurate" it is on average. While comparing outputs to historical data (option B) can help, historical data itself may be biased. Performance- related options (C and D) relate to efficiency and scalability, not fairness. Therefore, systematically assessing whether the training data covers all relevant end-user groups is the most direct and effective way to evaluate and mitigate bias.
References: AI Security Management™ (AAISM) Study Guide - AI Risk Identification and Impact Assessment; Data Governance and Bias Section.


NEW QUESTION # 284
A healthcare provider uses an AI system to support patient diagnoses. Physicians note recommendations are often accurate but limit their ability to justify treatment decisions to patients and regulators. Management argues that improved outcomes outweigh transparency concerns.
Which of the following is BEST for the information security manager to recommend?

Answer: D

Explanation:
Implementing explainable AI techniques and documenting model decision logic addresses the core concern of transparency while allowing the organization to continue benefiting from the AI system's diagnostic accuracy. This supports accountability, regulatory compliance, and informed communication with patients and regulators.


NEW QUESTION # 285
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