Cybersecurity Company Lumu Raises $8M, Signs Partnership with KnowBe4, the World's Largest Integrated Platform for Security Awareness Training

Actualizado el 4 de agosto, 2022 - 16.00hs.

Cybersecurity Company Lumu Raises $8M, Signs Partnership with KnowBe4, the World's Largest Integrated Platform for Security Awareness Training

PR Newswire

Round Led by Panoramic Ventures to Fund Growth Capital for Sales and Marketing Initiatives  

MIAMI, Aug. 4, 2022 /PRNewswire/ -- Lumu, creators of the Continuous Compromise Assessment cybersecurity model that empowers organizations to measure compromise in real time, today announced it has closed an $8 million investment round, bringing total funding to $15.5 million. Led by Panoramic Ventures, the investment will serve as growth capital for sales and marketing initiatives to further Lumu's mission of helping organizations operate cybersecurity proficiently. Other investors include KnowBe4 Ventures, Lane Bess, former Zscaler and Palo Alto Networks executive, and Tom Noonan, former CEO at Internet Security Systems and the SoftBank Group's SB Opportunity Fund. 

"We are excited to continue to support Lumu through this phase of hypergrowth, as organizations across all verticals are realizing the value of measuring compromise within their networks and acting on this factual data immediately," said Paul Judge, Managing Partner of Panoramic Ventures. "The innovation Lumu is bringing to the market is evident and a true game-changer for cybersecurity operations."

Lumu's Continuous Compromise Assessment model enables any organization to measure and understand compromise to close the breach detection gap from months to minutes continuously and intentionally. Teams receive actionable information about who was impacted, when the incident took place and how best to respond before it escalates to a bigger problem. The company has experienced hyper-growth in 2021 and 2022 and now has more than 3,100 organizations using its technology. The Lumu platform has analyzed more than 1 trillion metadata and detected more than 345 million adversarial contacts. 

"With today's economy, hiring constraints and the non-stop cyber threats, companies need tools that enable an accurate understanding of, and swift response to, potential attacks," said Ricardo Villadiego, Founder and CEO of Lumu. "Our platform provides context at the granular level to understand each and every incident and the specific techniques used by attackers so that cybersecurity operators can mitigate malicious incidents and overall improve their cybersecurity stack. With cybercriminals quick to take advantage of economic downturns, this funding round emphasizes just how critical of a time it is for enterprises to prioritize protection and defense mechanisms." 

The capital will also be used to scale the company's initiative to consistently attract exceptional talent to amplify the reach of Lumu's cyber industry-leading resilience message and to build credibility with target audiences to help companies of all sizes and verticals proficiently operate cybersecurity functions. 

KnowBe4 is one of the key investors joining Lumu's funding round. The companies will join forces to further their missions of enabling employees and security teams to make smarter security decisions every day. 

Miami-based Lumu is founded and led by Ricardo Villadiego, a successful second-time founder who is part of the SB Opportunity Fund's community of visionary Black, Latinx, and Native American entrepreneurs.

About Lumu

Headquartered in Miami, Florida, Lumu is a cybersecurity company focused on helping enterprise organizations illuminate threats and isolate confirmed instances of compromise. Applying principles of Continuous Compromise Assessment, Lumu has built a powerful closed-loop, self-learning solution that helps security teams accelerate compromise detection, gain real-time visibility across their infrastructure, and close the breach detection gap from months to minutes. Learn more about how Lumu illuminates network blind spots at www.lumu.io.

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SOURCE Lumu

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