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What you need to know about Confidential Computing

This blog includes content from Episode One “Confidentially Speaking” of our Cloud Security Podcast, hosted by Anton Chuvakin (Head of Solutions Strategy) and Timothy Peacock (Product Manager). You should listen to the whole conversation for more insights and deeper context.

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We all deal with a lot of sensitive data and today, enterprises must entrust all of this sensitive data to their cloud providers. With on-premises systems, companies used to have a very clear idea about who could access data and who was responsible for protecting that data. Now, data lives in many different places—on-premises, at the edge, or in the cloud. 

You may already know that Google Cloud provides encryption for data when it is in transit or at rest by default, but did you also know we also allow you to encrypt data in use—while it’s being processed? 

In this podcast episode, Product Manager Nelly Porter gave us a peek under the hood of  confidential computing at Google Cloud. 

What is confidential computing? 

Google Cloud’s Confidential Computing started with a dream to find a way to protect data when it’s being used. We developed breakthrough technology to encrypt data when it is in use, leveraging Confidential VMs and GKE Nodes to keep code and other data encrypted when it’s being processed in memory. The idea is to ensure encrypted data stays private while being processed, reducing exposure.

During the episode, Nelly Porter explained that Google Cloud’s approach is based on hardware and CPU capability. Confidential Computing is built on the newest generation of AMD CPU processors, which have a Secure Encrypted Virtualization extension that enables the hardware to generate encryption keys that are ephemeral and associated with a single VM.  Basically, they are never stored anywhere else and are not extractable—the software will never have access to those keys. 

“You can do whatever you need to do, but you will be in a cryptographically isolated space that no other strangers passing by can see.”

Memory controllers use the keys to quickly decrypt cache lines when you need to execute an instruction and then immediately encrypts them again. In the CPU itself, data is decrypted but it remains encrypted in memory. 

Confidential computing aims to mitigate gaps in data security

Nelly also shed some light on why confidential computing will continue to play a central role in the future of cloud computing. She pointed out that one of the biggest gaps companies are looking to cover is securing data when it is in use. 

Data that is encrypted on-premises or in cloud storage, but the biggest risk for companies is when they start working with that data. For instance, imagine you encrypted your data on-premises and only you hold the keys. You upload that data into Cloud Storage buckets—simple, safe, and secure. 

But now, you want to train machine learning models based on that data. When you upload it into your environment, it’s no longer protected. Specifically, data in reserved memory is not encrypted.

We’re trying to ensure that your data is always protected in whatever state it exists, so fewer people have the opportunity to make mistakes or maliciously expose your data.

Top takeaways about confidential computing 

Throughout the conversation, Nelly also shared interesting points about the development and direction of confidential computing at Google Cloud. 

Here were our favorite takeaways from the podcast: 

We worked hard to make Google Cloud’s approach simple.
We’ve invested a lot of time and effort into investigating the possibilities (and limitations) of confidential computing to avoid introducing residual risks to our approach. For instance, the early introduction of hardware capable of confidential computing in the industry required IT teams to have the resources to rewrite or refactor their app, severely limiting their ability to adopt it within their organizations. 

With Confidential Computing, teams can encrypt data in use without making any code changes in their applications.  All Google Cloud workloads can run as Confidential VMs, enabled with a single checkbox, making the transition to confidential computing completely simple and seamless. 

“A lot of customers understand the values of confidential computing, but simply cannot support re-writing the entire application. It’s why Google Cloud, in particular, decided to take a different approach and use models that were incredibly easy to implement, ensuring that our customers would not have those barriers to cross.”

Confidential computing is for more than just fintech. 
There is, of course, a compelling use case for confidential computing at highly-regulated companies in financial, government, life sciences, and public sectors. However, Nelly shared that her team didn’t anticipate that even verticals without significant regulation or compliance requirements would be so interested in this technology, mostly to pre-empt privacy concerns. 

Many companies see confidential computing as a way to create cryptographic isolation in the public cloud, allowing them to further ease any user or client concerns about what they are doing to protect sensitive data. For instance, during COVID-19, there was an increase in small research organizations that wanted to collaborate across large datasets of sensitive data. 

“Prior to confidential computing, it wasn’t possible to collaborate because you needed the ability to share very sensitive data sets among multiple parties while ensuring none of them will have access to this data, but the results will benefit all of them—and us.”

An open community, working together will be key for the future. 
Nelly also shared that there are plans to extend memory protections beyond just CPUs to cover GPUs, TPUs, and FPGAs. Google Cloud is working with multiple industry vendors and companies to develop confidential computing solutions that will cover specific requirements and use cases.

Confidential computing will not be achieved by a single organization – it will require many people to come together. We are a member of the Confidential Computing Consortium, which aims to solve security for data in use and includes other vendors like Red Hat, Intel, IBM, and Microsoft. 

“Google alone would not be able to accomplish confidential computing. We need to ensure that all vendors, GPU, CPU, and all of them follow suit. Part of that trust model is that it’s third parties’ keys and hardware that we’re exposing to a customer.”

There are no magic bullets when it comes to security. 
Confidential computing is still an emerging, very new technology and unsurprisingly, there are a lot of questions about what it does and how it works. It’s important to remember that there is no such thing as the one-tool-fits-all-threats security solution. Instead, Nelly notes that confidential computing is yet another tool that can be added to your security arsenal. 

“No solution will ever be the magic bullet that will make everyone happy and secure, guaranteed. But confidential computing is an addition to our toolbox of defense against gaps we have to take super seriously and invest in solving.” 

Did you enjoy this blog post? To listen to the full conversation, head over to Episode One “Confidentially Speaking” of our Cloud Security Podcast, hosted by Anton Chuvakin (Head of Solutions Strategy) and Timothy Peacock (Product Manager). 

We also recommend checking outother episodes of the Cloud Security Podcast by Google for more interesting stories and insights about security in the cloud, from the cloud, and of course, what we’re doing at Google Cloud.

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