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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:

A. AI tools are evaluated by the privacy department before implementation
B. Stakeholders have approved alignment with company values
C. The organization has implemented a responsible development policy
D. Outputs of AI tools do not perpetuate adverse biases

Answer: D

Explanation:
AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.
References:
AAISM Study Guide - AI Risk Management (Bias and Ethics Validation)
ISACA AI Security Management - Ethical AI Practices

NEW QUESTION # 21
Which of the following would BEST help mitigate vulnerabilities associated with hidden triggers in generative AI models?

A. Incorporating adversarial training to expose and neutralize potential triggers
B. Regularly retraining the model using a diverse data set
C. Applying differential privacy and masking sensitive patterns in the training data
D. Monitoring model outputs and suspicious patterns to detect trigger activations

Answer: A

Explanation:
Hidden triggers are adversarial backdoors planted in AI models, activated only by specific inputs. The AAISM materials specify that the best mitigation is to use adversarial training, which deliberately exposes the model to potential trigger inputs during training so it can learn to neutralize or resist them. Retraining with diverse data reduces bias but does not address hidden triggers. Differential privacy is focused on privacy preservation, not adversarial resilience. Monitoring outputs can help with detection but is reactive rather than preventative. The proactive solution highlighted in the study guide is adversarial training.
References:
AAISM Exam Content Outline - AI Risk Management (Backdoors and Hidden Triggers) AI Security Management Study Guide - Adversarial Training as a Mitigation Control

NEW QUESTION # 22
Which of the following is the MOST effective way to mitigate the risk of deepfake attacks?

A. Limiting employee access to AI tools
B. Using a general-purpose large language model (LLM) to detect fraud
C. Relying on human judgment for oversight
D. Validating the provenance of the data source

Answer: D

Explanation:
AAISM study content identifies validating the provenance of data sources as the most effective way to counter deepfake risks. Provenance validation ensures that content is authentic, verifiable, and traceable, preventing malicious synthetic media from being trusted as legitimate. Human oversight helps but cannot reliably detect sophisticated fakes. Limiting tool access reduces exposure but does not prevent external attacks. General-purpose LLMs are not optimized for fraud detection. The strongest control is verifying the origin and authenticity of data before acceptance.
References:
AAISM Study Guide - AI Risk Management (Deepfake and Content Integrity Risks) ISACA AI Security Management - Provenance Validation as a Defense

NEW QUESTION # 23
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?

A. Utilizing unstructured data sets
B. Adopting a more simplified model
C. Diversifying the model training data
D. Securing the model training data

Answer: C

Explanation:
AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may introduce new errors without addressing fairness. Securing training data protects confidentiality and integrity but does not resolve representational imbalance. Therefore, the best practice for reducing bias in ML is diversification of training datasets.
References:
AAISM Study Guide - AI Risk Management (Bias and Fairness in AI)
ISACA AI Security Management - Data Diversity and Representation Controls

NEW QUESTION # 24
Which of the following is the BEST approach for minimizing risk when integrating acceptable use policies for AI foundation models into business operations?

A. Implement responsible development training and awareness
B. Limit model usage to predefined scenarios specified by the developer
C. Establish AI model life cycle policy and procedures
D. Rely on the developer's enforcement mechanisms

Answer: C

Explanation:
The AAISM guidance defines risk minimization for AI deployment as requiring a formalized AI model life cycle policy and associated procedures. This ensures oversight from design to deployment, covering data handling, bias testing, monitoring, retraining, decommissioning, and acceptable use. Limiting usage to developer-defined scenarios or relying on vendor mechanisms transfers responsibility away from the organization and fails to meet governance expectations. Training and awareness support cultural alignment but cannot substitute for structured lifecycle controls. Therefore, the establishment of a documented lifecycle policy and procedures is the most comprehensive way to minimize operational, compliance, and ethical risks in integrating foundation models.
References:
AAISM Study Guide - AI Governance and Program Management (Model Lifecycle Governance) ISACA AI Security Guidance - Policies and Lifecycle Management

NEW QUESTION # 25
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