Protect Against Insider Threat With Anomaly Detection

How Splunk helps with this use case

Splunk User Behavior Analytics (UBA) leverages machine learning to baseline normal behavior for users and entities across the environment. This enables the proactive detection of subtle anomalies, such as unusual logins from new locations or devices, that traditional tools often miss.

UBA also provides automated investigation and response workflows, allowing security teams to quickly act on detected insider threats. This streamlines the process from detection to containment, reducing potential damage from malicious or negligent insider actions.

Finally, by identifying and flagging anomalous user behavior, UBA helps mitigate the financial risks associated with stolen or misused credentials. This capability prevents attackers from moving laterally undetected within the network, significantly strengthening an organization's overall security posture against insider threats.

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