Data volumes continue to increase exponentially, putting further strain on organizations already grappling with rising ingestion costs and resource constraints. Security teams are caught in a dilemma. Collect too much data and analysts are stuck sifting through the noise. Collect too little and risk a missed or delayed response to a critical security incident.
Capable of processing petabytes of pipeline data per hour from one, several or many sources, Auguria takes the first pass at analyzing security data, scoring and classifying events to underpin precise, dynamic and self-learning filtering, prioritization, and routing between your XDR, SIEM, or data lake.
Auguria compacts all data it ingests, identifying the most efficient ways to consolidate and aggregate information without losing any detail. Every event in every data stream is assigned a priority score, distinguishing “normal” from highly “abnormal”, and mapped to meaningful categories. Using these distinct criteria, SIEM architects and security engineers can effectively orchestrate security data flows. For example, routing compliance data to cost-efficient S3 storage while more critical alerts are sent to a platform like Splunk® for incident triage, management, and correlation.
By harnessing Auguria’s Security Knowledge Layer™ to automate the analysis of context-rich event data, organizations can significantly improve the return on investment and performance of existing security tools like SIEM, SOAR, and security data lakes, reining in consumption-based costs, and ultimately run more effective and cost-efficient security operations.
Organizations can slash their SIEM data volume by up to 99%, ensuring only the most relevant data reaches the SIEM. This precision reduces false positives and minimizes mean time to response (MTTR), resulting in a more effective and economical SOC. Auguria also simplifies data engineering by tailoring it for security operations, empowering your security team to own their data, and allowing them to focus on tackling threats, not wrangling data.