What is anomaly detection in AI?

Anomaly detection in AI refers to the identification of rare items, events, or observations that significantly differ from the majority of the data. This process is crucial in various applications such as fraud detection, network security, and quality control, where unusual patterns often indicate critical insights or potential issues. By focusing on these outliers, organizations can make informed decisions based on the anomalies identified, leading to improved security, risk management, or operational efficiency.

The other choices do not accurately describe the core function of anomaly detection. While improving model accuracy is beneficial, it does not specifically pertain to the identification of anomalies. Similarly, enhancing user engagement is related to user experience and behavior, not the identification of rare or significant data points. Lastly, describing anomaly detection as a standard way to process large datasets misses the distinguishing aspect of detecting deviations, which is a specific analytical goal rather than a general processing technique.