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NEW QUESTION # 38
Which of the following security framework elements BEST helps to safeguard the integrity of outputs generated by AI algorithms?
Answer: D
Explanation:
According to AAISM technical controls, the element of security frameworks that directly safeguards output integrity is ensuring that bias-related risks are maintained within acceptable ranges. This ensures that AI results remain consistent, fair, and reliable, preserving trust in outputs. Ethical awareness, architectural disclosure, and legal responsibilities are governance practices but do not directly secure output integrity.
Output integrity is primarily protected through bias management and ongoing evaluation of fairness and accuracy.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Integrity of AI Outputs) AI Security Management Study Guide - Bias and Output Integrity Controls
NEW QUESTION # 39
Which of the following types of testing can MOST effectively mitigate prompt hacking?
Answer: A
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 # 40
Which of the following factors is MOST important for preserving user confidence and trust in generative AI systems?
Answer: D
Explanation:
AAISM risk guidance underscores that transparent disclosure and informed consent are the most important factors in maintaining user trust in generative AI. Users must clearly understand how outputs are created, what data sources are used, and how risks such as bias or misinformation are managed. While bias minimization, access controls, and anonymization contribute to technical or ethical robustness, they are not sufficient to preserve user trust. Trust requires openness and consent, which align with governance expectations for transparency and accountability.
References:
AAISM Exam Content Outline - AI Risk Management (Transparency and Trust) AI Security Management Study Guide - User Confidence in Generative AI
NEW QUESTION # 41
Which of the following controls BEST mitigates the risk of data poisoning?
Answer: B
Explanation:
The AAISM technical controls framework emphasizes data validation as the primary safeguard against data poisoning attacks. Poisoning occurs when attackers insert malicious or corrupted data into training sets.
Validation techniques verify the quality, authenticity, and consistency of input data before training, preventing compromised samples from corrupting the model. Restoration helps after compromise, watermarking protects ownership, and intrusion detection monitors networks rather than data quality. The most effective preventive measure is data validation.
References:
AAISM Study Guide - AI Technologies and Controls (Data Poisoning Mitigation) ISACA AI Security Management - Data Validation and Quality Controls
NEW QUESTION # 42
When documenting information about machine learning (ML) models, which of the following artifacts BEST helps enhance stakeholder trust?
Answer: A
Explanation:
Themodel cardis a governance artifact that communicatesintended use, performance characteristics, limitations, fairness considerations, and ethical notesof an ML model.
AAISM governance materials highlight thatstakeholder trustcomes from transparency and explainability.
While hyperparameters and data quality controls are technical details, they lack stakeholder-facing clarity.
Model prototyping is part of development but not a governance record.
Model cards are explicitly recommended to:
* Provideexplainabilityand context for decision-making.
* Demonstrate governance, transparency, and compliance.
* Enable stakeholders (regulators, auditors, business leaders) to trust the system.
Therefore, themodel cardis the artifact that best enhances trust.
NEW QUESTION # 43
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