Which statement about anomaly detection in SecAI+ is most accurate?

• A. Anomaly detection is used to optimize model hyperparameters only.
• B. Anomaly detection is used to measure data quality after training.
• C. Anomaly detection helps identify adversarial inputs or unusual patterns and triggers alerts to mitigate risk.
• D. Anomaly detection replaces authentication controls.

Answer: (C)

Anomaly detection in SecAI+ is about spotting inputs or events that don’t fit what the system expects and taking protective action when something unusual is detected. The best description is that it helps identify adversarial inputs or unusual patterns and triggers alerts to mitigate risk. This fits because the core purpose of anomaly detection is to monitor for deviations that could indicate attacks, data tampering, or other security incidents, and to prompt a response such as alerts, investigation, or containment.

It’s not primarily about tuning model hyperparameters; those are part of model optimization, not anomaly detection. It isn’t mainly used to measure data quality after training; while it can flag unexpected data behavior, its central role is ongoing monitoring for irregularities rather than a post-training quality assessment. And it doesn’t replace authentication controls—authentication verifies who you are, whereas anomaly detection watches for unusual behavior to enhance security alongside existing controls.