Gain full security visibility by collecting, normalizing and storing all security-relevant data in a cost-effective and high performance data lake.
Tolerate undue risk by picking and choosing which logs you really need to ingest, in order to keep cost and performance at acceptable levels.
Detect threats in real-time by analyzing logs as they are ingested, giving you the fastest possible time to detection.
Delay running detections until data is at rest, extending the time that attackers have to pivot and exfiltrate data.
Get answers quickly with the ability to run queries over terabytes of data in minutes, not hours or days.
Wait hours or even days for large queries to complete, impeding critical incident investigation and response activities.
Write flexible, powerful detections using Python and standard CI/CD workflows that give you the alerts you need, while reducing noise.
Accept the limitations of proprietary detection languages that make writing, testing and maintaining complex detections challenging and inefficient.
Reduce SIEM costs dramatically while gaining lightning-fast query speeds, with an efficient, highly scalable serverless architecture.
Pay skyrocketing costs to keep up with the growth of cloud app data, due to expensive, legacy server-based architecture.
Focus efforts on security rather than infrastructure management with a security analytics platform delivered with no operational overhead.
Divert time and energy away from security to grapple with burdensome system administration, DevOps, and capacity planning.