Pass the Isaca Advanced in AI Audit AAIA Questions and answers with CertsForce

Ensuring AI model resilience against external threats involves validating that the model is configured to resist attacks, such as adversarial inputs, data poisoning, or misuse. The AAIA™ Study Guide emphasizes configuration testing as a crucial control to simulate threat scenarios and assess robustness.

“Model configuration testing simulates real-world threat conditions to validate model resilience. This includes testing against adversarial attacks, input manipulation, and exposure of sensitive outputs.”

While access monitoring (C) and anonymization (A) reduce risks, they don’t actively validate model behavior under threat conditions. Therefore, D offers the most effective resilience measure.

Including AI-specific disruption scenarios in the organization’s Disaster Recovery Plan (DRP) ensures preparedness for worst-case events affecting AI systems. The AAIA™ Study Guide recommends tailoring business continuity and DR strategies to cover model unavailability, data corruption, and AI-specific dependencies.

“AI disruptions can arise from unique causes such as model corruption, adversarial attacks, or drift. Integrating these into the DRP allows for effective, scenario-specific responses and minimizes downtime.”

Tabletop exercises (A) are valuable for preparedness but are less comprehensive than scenario planning. Kill chains (B) are security-specific. KRIs (C) aid in monitoring but don’t ensure recovery. Therefore, D offers the most robust mitigation.

In healthcare AI applications, protecting patient data is critical. The AAIA™ Study Guide identifies anonymization as one of the most effective strategies to preserve privacy and maintain data integrity. When combined with quality checks, it ensures data accuracy and compliance with health data protection regulations (e.g., HIPAA, GDPR).

“Anonymizing sensitive data removes identifying attributes, significantly reducing risk if data is accessed or leaked. Ongoing data quality checks ensure the integrity and utility of the anonymized dataset.”

While encryption (A) and access controls (C) are necessary technical safeguards, D provides the strongest dual assurance of privacy and accuracy. Option B focuses on model management rather than data security.

The AAIA™ Study Guide advises that if an AI model presents a risk that exceeds the organization's predefined risk tolerance—especially in cases of ethical harm or bias—deployment should be delayed until proper safeguards are in place. This approach prevents legal exposure and preserves stakeholder trust.

“When AI risks exceed acceptable thresholds, organizations must suspend implementation until corrective action reduces the risk to within tolerance levels. Proceeding without mitigation violates sound governance principles.”

While improving data (B) may help, it does not address the immediate governance concern. Risk tolerance (D) should not be adjusted to fit flawed systems. Thus, A is the correct course.

K-means clustering is a widely used unsupervised learning algorithm. However, it is sensitive to outliers and assumes that features are on the same scale, which can distort clustering results if not properly normalized. According to the AAIA™ Study Guide, this sensitivity can impact model reliability and the meaningfulness of clusters.

“Auditors should assess whether proper data preprocessing (e.g., normalization, outlier removal) was applied in clustering models. K-means assumes Euclidean distances, making it prone to errors when features differ in scale or contain outliers.”

Therefore, C correctly identifies the key risk.

Overfitting occurs when a model performs very well on training data but poorly on unseen data, indicating that the model has learned patterns specific to the training set rather than generalizing effectively. The AAIA™ Study Guide identifies overfitting as a common problem that impacts model reliability.

“Overfitting limits the model's applicability to real-world scenarios. It reflects excessive tailoring to the training data and poor performance on new, diverse inputs.”

Underfitting (A) would result in poor performance on both training and test data. Generalization (C) is the desired state, and bias (D) is a separate issue. Therefore, B is correct.

While cost, speed, and infrastructure are practical considerations, the AAIA™ Study Guide stresses that ethical implications must be the top priority. Implementing AI without evaluating potential harm, bias, discrimination, or privacy concerns can result in legal, social, and reputational damage.

“Ethical implications should be central to AI implementation decisions. Responsible AI practices involve evaluating societal impact, transparency, accountability, and fairness before deployment.”

Therefore, D is the most essential consideration.