ISACA Advanced in AI Audit AAIA Dumps in PDF

Free ISACA AAIA Real Questions (page: 4)

In the context of an AI implementation, which of the following actions is MOST critical for an organization's change management program?

  1. Conducting a comprehensive risk assessment specific to AI-related changes
  2. Reviewing documentation for AI system changes, updates, and patches
  3. Verifying that all employees have completed mandatory AI ethics training
  4. Ensuring the organization has a dedicated AI governance committee

Answer(s): A

Explanation:

The primary purpose of an AI acceptable use policy is to establish guidance on the ethical use of AI. It defines what constitutes responsible, compliant, and ethical behavior when interacting with or deploying AI systems within the organization.



During a pre-implementation risk assessment, an AI model is determined to present a significant risk of bias and potential harm in excess of the organization's risk tolerance. Which of the following is the MOST appropriate response?

  1. Obtain board approval for an exception.
  2. Enhance the data that the model is trained on.
  3. Revisit the risk tolerance to ensure it is appropriate.
  4. Postpone deployment until the risk can be safely managed.

Answer(s): D

Explanation:

The most appropriate response is to postpone deployment until the risk can be safely managed. Deploying an AI model that exceeds the organization's risk tolerance -- especially with known bias and potential harm -- would be irresponsible and non-compliant with governance principles. Risk must be mitigated before moving forward.



An organization is adopting AI for its procurement and inventory teams, raising concern from stakeholders that they will lose their jobs due to AI. Which of the following is the BEST way for the IS auditor to assess whether the potential negative impacts were minimized?

  1. Review human-centered design practices to determine how they were considered.
  2. Review how the project management team collected feedback in engagement activities.
  3. Review the current state assessment of how AI may impact the organization.
  4. Review the AI roadmap for short-term and long-term milestones.

Answer(s): A

Explanation:

The best way for the IS auditor to assess whether potential negative impacts were minimized is to review human-centered design practices. This approach ensures that AI adoption considered the needs, roles, and well-being of human stakeholders, helping to reduce resistance, mitigate job displacement concerns, and foster ethical implementation.



A healthcare organization uses data clustering to group patients by medical history for personalized treatment recommendations. Which of the following is the GREATEST privacy risk associated with this practice?

  1. Clustering increases the complexity of the model, making data harder to anonymize.
  2. The clustering requires more data, increasing the risk of a privacy breach.
  3. Irrelevant features in the data may result in inaccurate or biased treatments.
  4. Clusters can reveal sensitive personal information depending on how the information is presented.

Answer(s): D

Explanation:

The greatest privacy risk is that clusters can reveal sensitive personal information depending on how the information is presented. Even when data is grouped or anonymized, patterns in clusters may inadvertently expose identifiable or sensitive attributes, leading to privacy violations.



Which of the following is the GREATEST risk associated with using AI in audit planning?

  1. Scope creep
  2. Limited knowledge
  3. Increased planning costs
  4. Incomplete data

Answer(s): D

Explanation:

The greatest risk associated with using AI in audit planning is incomplete data. AI relies heavily on the quality and completeness of input data. If the data used during planning is incomplete, it can lead to flawed risk assessments, poor resource allocation, and an ineffective audit scope, ultimately compromising audit quality.



When using off-the-shelf AI models, which of the following is the MOST appropriate way for organizations to approach vendor management?

  1. Establish responsibility and clear terms for model updates and support.
  2. Only use models from vendors with globally recognized accreditation.
  3. Use the vendor only if the contract has been reviewed by the information security department.
  4. Ensure a minimum of three quotes have been obtained for market research and comparison.

Answer(s): A

Explanation:

The most appropriate approach to vendor management when using off-the-shelf AI models is to establish responsibility and clear terms for model updates and support. This ensures ongoing accountability, proper maintenance, and alignment with evolving business and compliance requirements throughout the model's lifecycle.



Which of the following metrics are the BEST indication of a mature and effective approach to an organization's data governance program for its AI systems?

  1. Total budget allocated to AI initiatives across all departments
  2. Number of AI projects completed within the last fiscal year
  3. Percentage of AI models with documented data lineage
  4. Frequency of data quality audits on the organization's data sets

Answer(s): C

Explanation:

The percentage of AI models with documented data lineage is the best indication of a mature and effective data governance program. It demonstrates transparency, traceability, and accountability in how data flows through AI systems, which is essential for compliance, auditing, and maintaining trust in AI outputs.



Which of the following is the BEST way to support the development and design of high-risk AI systems?

  1. Conduct regular training sessions for users on data privacy.
  2. Ensure the availability of trustworthy data sets.
  3. Regularly back up the AI system's data to a secure, offsite location.
  4. Implement multi-factor authentication (MFA) for all users accessing the AI system.

Answer(s): B

Explanation:

The best way to support the development and design of high-risk AI systems is to ensure the availability of trustworthy data sets. Reliable, high-quality data is foundational to building AI systems that are accurate, fair, and compliant -- especially critical in high-risk applications where errors can have significant consequences.



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