From the outside, Upwind Security looks like it’s had a smooth journey so far. Just four years in, the cloud security startup is now worth $1.5 billion, and boasts the likes of Siemens, Peloton, Roku, Wix, Nextdoor and Nubank among its clientele. But if you ask the company’s co-founder and CEO Amiram Shachar, the journey to get here was anything but certain.
“Three years ago, we would spend hours asking ourselves if we were heading in the right direction, and 80% of the time, it felt like we weren’t,” a candid Shachar told TechCrunch in an interview following the startup’s recent $250 million Series B.
“At the beginning, we constantly questioned whether the market needed our solution, whether it would be too hard to integrate into larger systems, or if customers would adopt it,” he recalled. “Developing a new approach was difficult; people are used to installing certain agents on machines, but they don’t like doing it.”
Upwind likes to call that approach “runtime” security: Prioritizing alerts and remediation efforts around threats and vulnerabilities in active services in real time. As Shachar puts it, it’s an “inside-out” take on cloud security, where internal signals like network requests and API traffic function as context to help security teams separate urgent risks from those that can wait.
Developing that approach wasn’t easy, however, as Shachar and his co-founders didn’t have a traditional background in security: they first built and sold a cloud compute brokerage called Spot.io, to NetApp for around $450 million in 2020.
“After joining NetApp post the Spot acquisition, I experienced first-hand how difficult cloud security really is,” Shachar said. “The security team would scan our environment and report issues, but they lacked critical context. Coming from a DevOps background, we (Shachar and his team) understood the infrastructure deeply, while security teams often didn’t know how APIs were exposed or which packages were running. As a result, they flagged many issues that weren’t real risks.”
But Shachar and his team felt they had better insight into cloud environments because they were running them. “The dominant approach was agentless, an ‘outside-in’ model where you scan environments externally,” he explained. “It’s easy to deploy, but it creates a lot of noise because you can only see what’s visible from the outside.”
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