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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Technologies and Controls38%- Privacy, ethics, trust, and safety controls
- AI lifecycle security: model selection, training, validation, and deployment
- AI security architecture and secure design principles
- Data management and protection controls for AI
- Security monitoring, testing, and continuous assurance for AI systems
Topic 2: AI Risk Management31%- AI risk assessment, thresholds, and treatment strategies
- AI threat and vulnerability management
- Regulatory compliance and ethical considerations in AI
- AI vendor and supply chain risk management
Topic 3: AI Governance and Program Management31%- Business continuity and incident response for AI systems
- AI asset and data lifecycle management
- AI security policies, standards, and procedures
- AI security program development and management
- AI governance frameworks and alignment with business objectives

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q269-Q274):

NEW QUESTION # 269
Which of the following is the MOST important consideration when an organization is adopting generative AI for personalized advertising?

Answer: B

Explanation:
In AAISM, usage of AI for activities involving personal data and profiling, such as personalized advertising, is explicitly mapped to stringent regulatory and compliance requirements (e.g., data protection, consent, profiling limitations, fairness obligations). The material notes that these activities may trigger "heightened regulatory scrutiny, mandatory impact assessments, and potential penalties for non-compliance."


NEW QUESTION # 270
A bank is training an AI credit scoring model on a vast amount of sensitive customer data. Which of the following is the MOST critical security risk in this training process?

Answer: A

Explanation:
Data poisoning is the most critical risk during the training process because attackers can intentionally manipulate training data to corrupt the model's behavior, accuracy, or decision- making outcomes. Compromised training data can fundamentally undermine the integrity and trustworthiness of the credit scoring model.


NEW QUESTION # 271
Which AI security risk is MOST likely to materialize if training data is sourced from untrusted vendors?

Answer: B

Explanation:
Training data obtained from untrusted vendors may contain maliciously manipulated or corrupted data designed to influence model behavior. This creates a high risk of poisoned data insertion, which can compromise the integrity, reliability, and security of the AI model.


NEW QUESTION # 272
A data scientist creating categories and training the algorithm on large data sets is an example of which of the following types of AI model learning techniques?

Answer: C

Explanation:
AAISM classifies learning paradigms by the presence of labeled targets. Creating categories (labels) and training on them is supervised learning, where input features are mapped to known outputs and optimization minimizes prediction error against ground truth.


NEW QUESTION # 273
What is the GREATEST concern when a vendor enables generative AI features for an organization's critical system?

Answer: B

Explanation:
AAISM highlights that uncontrolled access to generative AI in critical systems introduces the highest level of risk, as such models can:
* expose sensitive data
* execute unintended actions
* be manipulated through injected prompts
* cause operational instability
Monitoring (A) is important but not the core risk. Bias (B) is significant but secondary in critical systems.
Regulatory enhancements (C) are indirect.
References: AAISM Study Guide - Generative AI Operational Risk; Access Control Priority.


NEW QUESTION # 274
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