What is the goal of anomaly detection in Splunk?

Anomaly detection in Splunk focuses on identifying outliers or unexpected patterns in data. By definition, anomalies are deviations from the norm that can indicate unusual behavior or events that require further investigation. This process is crucial in cybersecurity, where outliers could represent potential threats, security breaches, or system malfunctions.

In terms of application, it analyzes various fields—both numerical and categorical—to pinpoint these unusual occurrences, allowing security analysts to act on the insights derived and to potentially mitigate risks. Hence, the primary aim is to detect these anomalies rather than merely predict future events, optimize resources, or summarize data. Identifying outliers serves as an essential step in proactive security measures, facilitating a timely response to protect systems and data.