
Versive
A provider of phishing threat management for organizations concerned about human susceptibility | previously known as Context Relevant.
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Total Funding | 000k |








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Versive, originally founded as Context Relevant in 2012 by Stephen Purpura and Chris Metcalfe, operated in the cybersecurity sector, specializing in machine learning to automate threat detection. The Seattle-based company secured a total of $79 million over nine funding rounds, including a notable $21 million Series B round in 2014. In 2016, Joseph Polverari, a seasoned executive with extensive experience in fintech, analytics, and AI from his time at Yodlee and Pandesic, took the helm as CEO. His leadership guided the company through its rebranding from Context Relevant to Versive in 2017.
The firm's core offering was the Versive Security Engine, a platform designed to identify and analyze active adversary campaigns within corporate networks. This system distinguished itself by moving beyond the standard practice of generating numerous alerts. Instead, it utilized machine learning to establish a baseline of normal network behavior, thereby automating security expertise to deliver a small number of high-fidelity "ThreatCases" weekly. This targeted approach allowed enterprise security teams to focus on substantial threats rather than being inundated with false positives. The business model centered on selling this technology through on-premises software, cloud services, and professional service solutions.
In October 2018, Versive was acquired by eSentire, a prominent Managed Detection and Response (MDR) provider. The acquisition aimed to integrate Versive's AI-driven threat detection capabilities into eSentire's MDR platform, enhancing their ability to manage and neutralize cyber threats at scale. The deal led to key Versive executives, including CTO Dustin Rigg Hillard, transitioning into senior leadership roles at eSentire to oversee the technology's integration and the evolution of the MDR service.
Keywords: cybersecurity, threat detection, machine learning, network security, adversary campaigns, managed detection and response, AI security, threat intelligence, data science, enterprise security