Anodot vs Splunk User Behavior Analytics comparison

Anodot provides a real time analytics and automated anomaly detection system that discovers outliers in vast amounts of data and turns them into valuable business insights. Analyzing the massive quantity of metrics generated by today’s businesses – manually or with traditional business intelligence tools – takes time and expertise. Using patented machine learning algorithms, Anodot isolates issues and correlates them across multiple parameters in real time, eliminating business insight latency and supporting rapid business decisions through its uncovered insights.

Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.