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ByzGen: proudly partnering with CYSEC

Our DLT platform FALKOR runs well and securely on the public cloud. But partnering with CYSEC means we can go one step further in protecting key material and cryptographic secrets, with an additional confidential compute layer...
Terry Leonard
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Terry Leonard
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Here at ByzGen we’ve partnered with CYSEC to enhance the capability of our DLT platform, FALKOR in certain use cases and environments.

FALKOR runs perfectly on the public cloud. But CYSEC offer secure, agile and confidential computing solutions, which include areas where our container based FALKOR platform can be deployed within a trusted environment. 

The solution also offers a secure hardware base, including a certified Hardware Security Module that can be used to protect the key material and cryptographic secrets that are generated by the FALKOR platform. 

An additional confidential compute layer 

The partnership with CYSEC allows ByzGen to leverage an additional infrastructure layer for FALKOR in combination with the typical public cloud deployment, to provide a secure compute environment with hardware based crypto services.

This combination is important within regulatory environment settings. And where security policies around the data being processed means that a public, ‘cloud only’ model can’t be used.  

We’ve also seen how important this additional confidential compute layer is, when data from different parties needs to be processed, analysed, or modelled – but the privacy and audit of that data needs to be maintained as it goes through the processing, analysis, or modelling. 

How to make a secure compute and audit environment

The diagram below helps to explain how the secure compute and audit environment we’ve been talking about, can be created.

This secure environment can then be used for multiple third parties to input data, have that data processed or analysed, and then securely push the output back to the third parties who need to receive it. All while preserving the privacy of the data. Plus, a full validated audit of how the data is used in the environment can be tracked. 



Who is this helpful for?

The main use cases we’ve been discussing for this combined service include distributed secure analytics, and distributed privacy preserving machine learning ecosystems. Basically, systems where multiple entities need to contribute data to the analytics or machine learning – but want to maintain privacy, control, and audit over that data. As well as benefiting from the output of the analytics or machine learning. 

If this sounds interesting to you, and DLT solutions are something you’ve been exploring for your business – we are always happy to chat. Just get in touch below.

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