Exabeam Security Management Platform

Security Information and Event Management (SIEM) emerged in 2005 as a promising answer to enterprise security by centralizing security data for faster analysis and response. Over time, traditional SIEM tools showed major shortcomings: growing threat volume created alert fatigue, investigations stayed largely manual, incidents often lacked business context, and analysts struggled to prioritize risk. Rising storage costs for massive log ingestion further pushed organizations to seek alternatives.

Advances in big data analytics and machine learning enabled security products that reduce noise, correlate related signals, enrich alerts with context, and provide actionable guidance. Alongside these specialized tools, SIEM platforms themselves began evolving into “next-generation” solutions with more automation and intelligence. Exabeam, founded in 2013 in Foster City, California by cybersecurity veterans, positions itself as a “Smarter SIEM” and has evolved from a UEBA add-on into a modular security management platform that can replace or augment existing SIEM and SOAR deployments. Deployment is flexible (on-premises, cloud, as-a-service, or via MSSP), and licensing is user-based rather than tied to data volume.

Key modules include Exabeam Data Lake (introduced in 2017) for centralized storage on a big-data architecture with flat user-based pricing and Elastic Stack foundations, plus compliance reporting and natural-language support for rule policies. Cloud visibility is expanded through Exabeam Cloud Connectors (from the SkyFormation acquisition), supporting logs from 30+ cloud and SaaS services with vendor-managed updates. Exabeam Advanced Analytics focuses on behavior modeling and multi-source correlation, presenting findings as “incidents” with reconstructed timelines and evolving risk scores. MITRE ATT&CK mapping supports investigation and threat hunting, while risk profiles, peer groups, and identity attribution help track lateral movement and asset ownership. Additional modules extend analytics to IoT/OT devices, and SOAR capabilities (Incident Responder, playbooks, triggers) enable partially or fully automated workflows such as end-to-end phishing response. Threat Hunting unifies searching with the same timeline-based context used for detected incidents.

• Security Information and Event Management (SIEM) was first introduced as a concept in 2005.
• Traditional SIEM deployments have produced alert fatigue due to large volumes of notifications.
• Exabeam is a privately owned security analytics vendor headquartered in Foster City, California.
• Exabeam was founded in 2013 by cybersecurity veterans from Imperva, ArcSight, and Sumo Logic.
• Exabeam was originally released in 2015 as a UEBA product intended to complement existing SIEM platforms.
• Exabeam Data Lake was introduced in 2017.
• Exabeam Data Lake is designed on top of the Elastic Stack.
• Exabeam acquired SkyFormation and integrated the technology as Exabeam Cloud Connectors.
• Exabeam Cloud Connectors support security log collection from over 30 cloud services, including AWS, Google Cloud, Salesforce, and Office 365.
• The MITRE ATT&CK framework has been maintained by a non-profit organization since 2015.

• “Traditional SIEM products turned out to be somewhat less perfect than anticipated.”
• “Security experts were struggling with alert fatigue, overwhelmed by sheer numbers of notifications produced by a SIEM.”
• “The company itself uses the term ‘Smarter SIEM’ to market their solution.”
• “Companies no longer need to make a choice between longer retention periods and storage cost savings.”
• “Notably, Exabeam platform does not deal with ‘alerts’ or individual security events like traditional SIEM products: instead, its UI is designed around the notion of ‘incidents’.”
• “An incident is a chain of related events and alerts that the platform automatically links together and places along a reconstructed timeline.”
• “Analysts can even directly search for specific MITRE tactics and techniques, looking directly for the root cause of a potential attack.”
• “Users with similar risky behavior patterns are automatically classified into dynamic peer groups to further optimize the detection of anomalous behaviors.”
• “This not only dramatically simplifies tracking hacker’s lateral movements through the corporate network but helps reliably identify owners of any asset involved in an incident.”
• “A powerful playbook engine allows automating forensic and response processes with high-level action blocks.”