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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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q311-Q316):

NEW QUESTION # 311
An organization implements AI-driven behavioral analytics to identify insider threats and compromised credentials. Which of the following is the MOST significant security risk to be addressed by this deployment?

Answer: D

Explanation:
Adversaries can attempt to manipulate behavioral analytics models by injecting misleading patterns or adapting their behavior to evade detection, which can result in inaccurate threat identification and undermine the effectiveness of the system.


NEW QUESTION # 312
Which of the following BEST describes an adversarial attack on an AI model?

Answer: A

Explanation:
In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se.
Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation.
References:AI Security Management™ (AAISM) Body of Knowledge: Taxonomy of AI Threats- Adversarial Examples and Evasion; Distinction from System/Network Attacks.AAISM Study Guide:
Definitions and Threat Models in Adversarial ML; Attack-Defense Mapping for Model Predictions.


NEW QUESTION # 313
Which of the following types of testing can MOST effectively mitigate prompt hacking?

Answer: C

Explanation:
Prompt hacking manipulates large language models by injecting adversarial instructions into inputs to bypass or override safeguards. The AAISM framework identifies adversarial testing as the most effective way to simulate such manipulative attempts, expose vulnerabilities, and improve the resilience of controls. Load testing evaluates performance, input testing checks format validation, and regression testing validates functionality after changes. None of these directly address the manipulation of natural language inputs.
Adversarial testing is therefore the correct approach to mitigate prompt hacking risks.
References:
AAISM Exam Content Outline - AI Risk Management (Testing and Assurance Practices) AI Security Management Study Guide - Adversarial Testing Against Prompt Manipulation


NEW QUESTION # 314
A financial organization is concerned about AI data poisoning. Which control BEST mitigates this risk?

Answer: D

Explanation:
AAISM outlines that diversifying training data sources reduces the likelihood and impact of poisoning because:
- attack samples become harder to inject
- anomalies are easier to detect
- corrupted data has reduced influence on model behavior


NEW QUESTION # 315
Which of the following would BEST protect an AI system against prompt injection attacks?

Answer: D

Explanation:
Context awareness mechanisms help the AI system recognize malicious, irrelevant, or manipulative prompts that attempt to override instructions or alter intended behavior. This provides direct protection against prompt injection attacks by enabling the system to evaluate and constrain prompt context before responding.


NEW QUESTION # 316
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