Edgeless Systems Raises $5M to Advance Confidential Computing
Confidential computing will revolutionize cloud security in the decade to come and has become a top C-level priority for industry leaders such as Google, Intel and Microsoft. Edgeless Systems is leading these advancements to ensure all data is always encrypted.
March 8, 2023
PRESS RELEASE
BOCHUM, Germany, March 7, 2023 /PRNewswire/ -- Edgeless Systems, a pioneering confidential computing company that is turning the public cloud into the safest place for sensitive data, today announced it has raised $5 million in its seed round led by Berlin-based SquareOne with angel investors that include Evan Weaver (founder of Fauna), Mirko Novakovic (exit of Instana to IBM), Paolo Negri (founder of Contentful), Mathias Biilman and Chris Back (founders of Netifly).
The company is also hosting the annual Open Confidential Computing Conference (OC3) on March 15, 2023 where CTOs from Intel, Microsoft, AMD, Nvidia and others will discuss the state of confidential computing.
"Too many companies remain locked out of the cloud due to security concerns, but confidential computing technologies are poised to dramatically address this challenge in the coming months and years," said Georg Stockinger, Partner, SquareOne. "Edgeless Systems is already light years ahead of anyone else building the tools to make confidential computing easily accessible for developers in the enterprise. Its Constellation platform for Kubernetes is proof of its deep technical knowledge and first-mover position."
Edgeless Systems' flagship open source platform, Constellation and its associated tools, are already being used by hundreds of developers and DevOps engineers at companies such as Bosch, IBM and Intel, among others. Constellation is a confidential orchestration platform for Kubernetes. It leverages confidential computing to isolate and runtime-encrypt entire Kubernetes deployments, finally allowing enterprises to use the public cloud like their private cloud.
"We are turning the public cloud into everyone's private cloud," said Felix Schuster, CEO of Edgeless Systems. "By encrypting data all the time, even at runtime, and providing the best possible protection against infrastructure-based threats like malicious admins or co-tenants, Edgeless Systems can transform the way developers build and secure their public cloud workloads."
Moving workloads to the public cloud has many proven benefits but raises major compliance and data security concerns. Unfortunately, data leaks and compliance violations are realities associated with cloud transformation. Confidential computing is a hardware-based technology that shields computer workloads from their environments and keeps data encrypted even during processing. The required hardware (largely big hyperscalers) to realize the promise of confidential computing has only recently become available. Edgeless Systems has been an active driver of this emerging technology and today brings to bear both the technologies and financial backing to take it to the next level.
Edgeless Systems will use the seed funding to sustain its product lead, expand into the United States and add exciting features to its Constellation platform. It aims to be the defacto standard for 100 percent secure Kubernetes in the coming years. It will also build out its marketing and sales teams to help educate the industry on confidential computing and support advancements in this area.
For more information about Edgeless Systems, visit GitHub or get in touch for a live demo.
About Edgeless Systems
Edgeless Systems develops leading open source software for confidential computing to make the cloud the safest place for sensitive data. Edgeless Systems' Constellation is the first Kubernetes platform to ensure that hackers, cloud admins and foreign governments cannot access any data. The company was founded in 2020 by industry experts in Bochum, Germany, and has renowned investors and customers such as the Swiss stock exchange SIX and Bosch. For more information, please visit: https://www.edgeless.systems/
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