With a growing threat landscape, widening attack surface, and the increasing sophistication of attacks, threat detection and response has become increasingly challenging for security teams. While multiple security solutions are deployed in most organizations, serious threats continue to avoid detection because data is collected and analyzed in silos.
Organizations have attempted to solve this issue using SIEM systems as an aggregation tool. ESG research tells us that 88% of organizations are either already running a SIEM or have plans to.1 However, traditional SIEMs have become expensive to own and operate, often requiring significant upfront costs. Recent announcements by Google/Chronicle Backstory and Microsoft Sentinel offering cloud-native SIEM tools with unlimited data and analytics processing have reinforced the need to consolidate and analyze massive amounts of telemetry from the many security solutions, including endpoint security, network security, email security, and cloud security. While these cloud-delivered SIEM replacements eliminate the expensive SIEM storage issues, they still lack the context needed to accelerate detection and response times. SIEM systems lack ML-based correlation and analysis capabilities, leaving this effort to the already overworked SOC analysts.