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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q36-Q41):
NEW QUESTION # 36
Personal data used to train AI systems can BEST be protected by:
- A. Ensuring the quality of personal data
- B. Hashing personal data
- C. Erasing personal data after training
- D. Anonymizing personal data
Answer: D
Explanation:
AAISM guidance on privacy-preserving AI highlights anonymization as the most effective means of protecting personal data used in training. By irreversibly removing or masking identifiable attributes, anonymization ensures that training data cannot be linked back to individuals, thereby meeting key privacy obligations under laws such as GDPR. Erasing data after training may limit exposure but does not protect it during the training process. Ensuring data quality improves accuracy but does not mitigate privacy risk.
Hashing protects data integrity but does not guarantee anonymity, as hashes can sometimes be reversed or correlated. Therefore, anonymization is the recommended control for protecting personal data in AI training.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Methods) ISACA AI Security Management - Data Anonymization Practices
NEW QUESTION # 37
An organization's CIO provided the AI steering committee with a list of AI technologies in use and tasked them with categorizing the technologies by risk. Which of the following should the committee do FIRST?
- A. Begin grouping similar AI products and solutions together
- B. Assess risk levels based on risk appetite and regulatory requirements
- C. Ensure the AI technologies are included in the asset inventory
- D. Identify vulnerabilities related to the technologies in use
Answer: C
Explanation:
AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance. Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.
References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories as a Prerequisite to Risk Analysis) ISACA AI Security Management - Asset Visibility and Risk Categorization
NEW QUESTION # 38
When an attacker uses synthetic data to reverse engineer an organization's AI model, it is an example of which of the following types of attack?
- A. Inversion
- B. Prompt
- C. Poisoning
- D. Distillation
Answer: A
Explanation:
AAISM defines model inversion attacks as those where adversaries use queries or synthetic data to reconstruct sensitive information or approximate the inner workings of a model. By exploiting outputs, attackers attempt to reverse engineer training data or model functionality. Distillation refers to compressing models, not adversarial attacks. Prompt attacks relate to manipulating language model inputs, and poisoning occurs when adversaries corrupt training data rather than infer from outputs. The scenario describes attackers using synthetic data to reveal hidden characteristics, which aligns directly with inversion attacks.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Attack Types and Mitigations) AI Security Management Study Guide - Model Inversion Risks
NEW QUESTION # 39
Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?
- A. Encrypting data in transit and at rest
- B. Enforcing least privilege access
- C. Implementing data sanitization techniques
- D. Conducting adversarial testing
Answer: C
Explanation:
AAISM materials make clear that the best safeguard against sensitive information being leaked through the outputs of LLMs is data sanitization. This involves filtering, redacting, or masking sensitive content before the model can use it, thereby preventing unintended disclosure in outputs. Encryption protects confidentiality in storage and transmission but does not stop output leaks. Adversarial testing helps identify vulnerabilities but does not prevent exposure by itself. Least privilege access restricts who can interact with the model but does not sanitize the content of its outputs. The control most directly tied to preventing leakage is implementing data sanitization techniques.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Data Leakage Prevention) AI Security Management Study Guide - Sensitive Data Controls in Generative AI
NEW QUESTION # 40
Which of the following key risk indicators (KRIs) is MOST relevant when evaluating the effectiveness of an organization's AI risk management program?
- A. Number of AI models deployed into production
- B. Number of AI-related training requests submitted
- C. Percentage of critical business systems with AI components
- D. Percentage of AI projects in compliance
Answer: D
Explanation:
AAISM identifies percentage of AI projects in compliance as the most relevant KRI for evaluating AI risk management effectiveness. This metric directly reflects adherence to governance, regulatory, and security requirements. The number of models deployed (A) or systems with AI components (B) indicate scale, not risk management quality. Training requests (D) show awareness levels but do not measure effectiveness of risk management. Compliance percentage provides a direct, measurable indication of how well risks are being governed and mitigated.
References:
AAISM Exam Content Outline - AI Risk Management (Risk Metrics and Compliance) AI Security Management Study Guide - Key Risk Indicators in AI Programs
NEW QUESTION # 41
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