empow
empow was an Israeli cybersecurity startup that developed an AI-powered security analytics platform using natural language processing and machine learning to automatically classify, investigate, and respond to security threats—transforming how SIEMs and security tools interpret and act on alerts.
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empow developed a security analytics platform that applied natural language processing (NLP) and machine learning to automatically classify security alerts, correlate events across multiple security tools, and generate automated response actions. The platform's key innovation was using NLP to understand the intent behind security events—similar to how NLP understands natural language—enabling more accurate threat classification than traditional rule-based SIEM correlation. This 'intent-based' approach reduced false positives and enabled more effective automated response.
Commercially, empow operated in the security analytics and SIEM enhancement market alongside Exabeam, Securonix, and LogRhythm. Founded in 2014 in Ramat Gan, Israel by Avi Chesla (CEO, former CTO of Radware and ex-IDF intelligence) and a team of security veterans, the company raised $19M from investors including Ascent Venture Partners. In July 2021, Cybereason acquired empow to enhance its XDR (Extended Detection and Response) platform with empow's predictive response technology and data integration capabilities.
From a defense and national security perspective, AI-powered security analytics with intent-based threat classification is directly relevant to military and government SIEM operations where alert volumes overwhelm analyst capacity. NLP-driven threat understanding enables more intelligent automated triage and response, reducing the skilled analyst bottleneck in defense SOC operations. The founder's background as former CTO of Radware (a major Israeli defense-adjacent cybersecurity firm) reinforces dual-use credentials.
Dual-Use Assessment
AI-powered intent-based security analytics directly applies to military SIEM operations, enabling intelligent automated threat classification and response in defense SOCs overwhelmed by alert volumes. NLP-driven threat understanding reduces skilled analyst dependency.
Key Technologies
- NLP-based intent classification for security events
- Machine learning threat correlation across multi-vendor security tools
- Predictive response technology for automated threat remediation
- Out-of-the-box data integrations for diverse security tool ecosystems
- Automated alert triage and false positive reduction
- Security analytics enrichment for existing SIEM platforms
Use Cases & Applications
- SIEM enhancement with AI-powered alert classification
- Multi-tool security event correlation and threat investigation
- False positive reduction through intent-based threat analysis
- Automated security response workflow generation
- Military/government SIEM intelligent automation and triage (dual-use)
- Defense SOC analyst augmentation through NLP-driven threat understanding (dual-use)
Strategic Value to U.S.-Israel Alliance
Intent-based security analytics addresses the fundamental alert overload challenge in defense SOC operations. NLP-driven threat classification reduces reliance on scarce skilled analysts while improving threat detection accuracy.
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