Which of the following is the PRIMARY control that should be in place when creating and collecting data for the AI system?
Answer(s): B
Documenting the intended purpose of the data and validating its relevance ensures that data collection is aligned with defined business objectives, legal use limitations, and ethical boundaries, forming the foundational governance control for lawful, appropriate, and risk-aware AI system development.
Which of the following is the MOST important consideration when engaging a vendor to develop a customized AI model?
Answer(s): C
A clear definition of the model’s specific objectives and intended uses ensures proper alignment with business needs, risk boundaries, governance requirements, and control design, forming the foundation for ethical deployment, accountability, and effective assurance of a customized AI model.
An IS auditor is assessing the effectiveness of an organization’s AI governance framework.Which of the following presents the GREATEST risk to AI governance when deploying a new AI system across multiple departments?
Prioritizing rapid deployment and scalability over governance introduces significant risk because it bypasses proper controls, risk assessment, accountability structures, and oversight mechanisms, leading to fragmented implementation, unmanaged risk exposure, and weakened governance effectiveness across departments.
Which of the following should be done FIRST when establishing data governance for AI solutions?
Answer(s): D
Completing an inventory of existing data establishes visibility, ownership, sources, sensitivity, and usage context, which is foundational for building any effective data governance framework, enabling proper classification, controls, policies, and risk management for AI solutions.
An organization is initiating a digital transformation strategy and is considering the adoption of an AI governance framework.Which of the following types of risk is MOST important for the framework to mitigate?
Answer(s): A
Ethical risk is foundational in AI governance because it directly affects trust, fairness, accountability, transparency, and societal impact, shaping how AI systems are designed, deployed, and accepted during digital transformation and influencing regulatory, reputational, and stakeholder risk across the organization.
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