Isaca Updated AAIA Exam Questions and Answers by ocean

When a system is actively producing harmful content, the priority is " immediate containment. " " Enabling content output filters " is a critical safety control that acts as a " guardrail, " blocking the harmful responses before they reach the end-user. While prompt sanitization (Option A) targets the input , harmful advice often stems from the model ' s internal logic regardless of the input. Output filters provide the fastest mitigation for protecting users and the organization ' s reputation. Once the immediate risk is contained, the organization can conduct a root-cause analysis and perform more permanent retraining or access segmentation.

The ISACA AAIA™ framework distinguishes between " Verification " and " Validation " . " Verification " is a QA process that checks if the system was " built right " —i.e., whether the code, parameters, and architecture match the design documentation. " Validation " (Option A) checks if the " right system " was built—i.e., whether the model actually performs correctly on unseen data and meets business goals. While testing (Option C) identifies the performance gap, the specific act of auditing the model ' s configuration against its technical blueprints is a verification activity.

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.

Differential privacy introduces carefully calibrated noise during training or query responses so that it becomes mathematically difficult to infer whether any specific individual’s record is included in the training set. For healthcare data—highly sensitive and subject to strict privacy laws—this technique directly supports privacy-by-design, reducing the risk that model outputs leak membership information or reconstruct personal records.

Option A uses real patient records directly and does not, by itself, mitigate inference risk. Option B (data augmentation) may expand the dataset but does not guarantee resistance to membership inference attacks. Option D (transfer learning using public data) can help, but if any private data is used in fine-tuning, privacy risks remain. Differential privacy, as in option C, is the most appropriate control to ensure that outputs do not reveal whether particular personal data was used.