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.LEARN MORECloud Security Podcast – Confidentially SpeakingListen to the Cloud Security Podcast with guest Nelly Porter, Group Product Manager @ Google.

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 out other 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.

Today, we are happy to announce that Confidential GKE Nodes are available on compute optimized C2D VMs.

Many companies have adopted Google Kubernetes Engine (GKE) as a key component in their application infrastructure. In some cases, the advantages of using containers and Kubernetes can surpass those of traditional architectures, but migrating to and operating apps in the cloud often requires strategic planning to reduce risk and prevent data breaches. This is where Confidential GKE Nodes can be utilized to enhance the security of your GKE clusters or node pools.

Confidential GKE Nodes leverage specialized hardware to encrypt data in-use and are ideal for organizations processing sensitive data in the cloud. To make it easier to start using Confidential GKE Nodes, GKE standard workloads you run today can run as confidential GKE workloads without code changes on your end.

Security underpinnings of Confidential GKE Nodes

As we expand the Confidential Computing product portfolio from Confidential VMs to Confidential GKE Nodes to Confidential Dataproc, ensuring high performance is key. Confidential GKE Nodes are built on the same technology foundation as Confidential VM and utilize the Secure Encrypted Virtualization (SEV) capability of AMD EPYC™ processors. This feature allows you to keep data encrypted in memory with node-specific, dedicated keys that are generated and managed by the processor. The keys are generated in hardware during node creation and reside solely within the processor, making them unavailable to Google Cloud or other nodes running on the host.

Combined with the high performance of C2D VMs

Previously, Confidential GKE Nodes were generally available only on general purpose N2D VMs, but now they’re also available on compute optimized C2D VMs. The C2D machine series provides VM sizes ranging from 2 vCPUs to 112 vCPUs, offers up to 896 GB of memory, and are suited for performance-intensive workloads. C2D standard and C2D high-CPU machines serve compute-bound workloads including high-performance web servers and media transcoding. C2D high-memory machines serve specialized workloads such as high-performance computing (HPC) and electronic design automation (EDA), which require more memory.

Confidential GKE Nodes on compute-optimized C2D VMs could be a fit for use cases that require high performance and security. You can achieve encryption in-use for data processed inside your GKE cluster or just on specific node pools, without significant performance degradation. This is relevant for industries such as financial services, healthcare, retail, blockchain, and telecommunications, which often have sensitive data or personally identifiable information (PII) that requires additional security measures.

How MATRIXX used Confidential GKE Nodes

MATRIXX Software chose Confidential GKE Nodes to provide transparent encryption for data in-use to supplement encryption for data at-rest to secure personal subscriber data as required by privacy regulations.

MATRIXX Digital Commerce Platform (DCP) is a real-time 5G monetization for the communications industry, serving many of the world’s largest operator groups, regional carriers, and emerging digital service providers. MATRIXX used Google Cloud Confidential GKE Nodes to deliver a cloud-first digital commerce solution that enables commercial and operational agility for current and new telco business models.

A whitepaper titled “Protecting Your 5G Revenue Stream in the Cloud,” described how when MATRIXX DCP is deployed with Confidential Computing on Google Cloud, “its subscriber data, account balances, network events and charges/revenue streams are encrypted in use without making any code changes to the application or compromising on performance.”

Confidential GKE Nodes are globally available

At Google Cloud, we’re committed to investing in Confidential Computing, so we’ve expanded our support to VM families like C2D VMs. Confidential GKE Nodes running on C2D VMs are available in regions across the globe, including us-central1 (Iowa), asia-southeast1 (Singapore), us-east1 (South Carolina), us-east4 (North Virginia), asia-east1 (Taiwan), and europe-west4 (Netherlands). Note that Confidential GKE Nodes are available where C2D or N2D machines are available.

Pricing for Confidential GKE Nodes

There is no additional cost to deploy Confidential GKE Nodes, other than the costs of Compute Engine and Confidential VM pricing.

Try out Confidential GKE Nodes for cluster-level enablement

• First, go to the Google Kubernetes Engine page in the Google Cloud console. In the top navigation bar, click Create. In the Create Cluster modal, choose ‘Standard: You manage your cluster’ and click Configure.
• Next, from the left navigation pane, under Cluster, click Security. Select the ‘Enable Confidential GKE Nodes’ checkbox.
• Then, from the left navigation pane again, under Node Pools, click Nodes. Under Machine Configuration and Machine family, select the Compute-optimized tab, and choose a C2D machine type.

Configure the rest of the cluster as desired and click Create.

Making secure design choices should be easy, especially when the workloads involve high-performance processing of sensitive data. You can help protect your sensitive applications and data today by adding Confidential GKE Nodes to your GKE workloads. Learn more about Confidential Computing here.

To help secure increasingly complex and dynamic cloud environments, many security teams are turning to attack path analysis tools. These tools can enable them to better prioritize security findings and discover pathways that adversaries can exploit to access and compromise cloud assets such as virtual machines, databases, and storage buckets.

Other attack path tools rely on static, point-in-time snapshots of an organization’s cloud footprint, which often contain sensitive data about their environment, how it is configured, and where the most sensitive data resides.

At Google Cloud, we are taking a different approach. We are excited to announce today at the Google Cloud Security Summit that we are adding attack path simulation to Security Command Center, our built-in security and risk management solution for Google Cloud. This new risk management capability automatically analyzes a customer’s Google Cloud environment to pinpoint where and, importantly, how vulnerable resources may be attacked, so security teams can stay one step ahead of adversaries.

We expect attack path simulation capabilities to be available in Security Command Center Premium later this summer.

Unlike some third-party security products, Security Command Center continuously scans an organization’s cloud environment gathering near real-time data about cloud resources and security vulnerabilities. Our attack path simulation engine uses this information to automatically generate and render high-risk attack paths, without the hands-on toil of having to repeatedly run manual queries.

A different approach to attack path analysis

Other attack path analysis tools involve significant operational toil. The static, point-in-time snapshots that these tools generate have to be sent to an external provider, which can add risk. Then security teams have to follow up with complex queries before they can identify likely attack paths. 

Google Cloud’s advanced attack simulation engine leverages our first-party, agentless visibility of Google Cloud assets, the relationships between assets, and the current state of defenses. Attack path simulation is fully automated with no need to manually run queries. Simulations run in the Google Cloud environment, and do not send snapshots outside your environment, avoiding exposure of sensitive information.

See what attackers can see

Effective attack path analysis should mimic how a real-world attacker can reach and compromise high value resources. This is why Security Command Center simulates how attackers try many different ways to infiltrate a cloud environment. SCC then generates attack path graphs to give defenders insight into how adversaries could exploit a single security weakness or various combinations of security vulnerabilities to access valuable assets. SCC also provides detailed information on how to remediate issues and shore up defenses based on its findings

We are delivering combined attack path simulation and analysis as a managed service. There are no agents to install or manage. Results automatically reflect changes in your organization’s Google Cloud environment. Because our attack path simulations are conducted on models of an organization’s cloud resources, there is no performance or operational impact to the live production environment.

Better security prioritization

Security Command Center automatically computes an attack exposure score for misconfigurations and vulnerabilities that expose valuable resources to attackers. The score is a measure of cyber risk. It takes into account how exposed valued resources are, and the paths of least resistance for attackers to reach those resources. 

Security teams can use these scores to prioritize remediation efforts and improve their overall risk posture.

How attack path analysis has already reduced risk for customers 

Dozens of customers have already used our attack path capabilities in private Preview to improve their security posture and reduce their operational risk.

Security Command Center alerted one customer to a finding with a high attack exposure score. The finding was related to a service account whose keys were not being rotated. After reviewing the attack paths related to this finding, the cloud security manager discovered that even though the service account was named “test,” it provided access to storage buckets outside of the test environment. 

If an attacker had been able to steal the credentials for this test account, they could have easily accessed production data. The security manager removed administrator privileges on the account. The attack path simulation enhanced their understanding of the severity of the finding, and helped convince them to make it a high-priority fix.  

Another customer using attack path simulation needed to assess which security findings created the greatest risk. Two findings related to the same service account with high exposure scores rose to the top of the list. The attack paths revealed that the service account had access to more than 500 storage buckets. If an attacker were to gain access to this account they would be able to read, write, or delete data across any of those buckets, many of which contained sensitive business data and confidential customer information.

Using Security Command Center’s attack path results, the security team remediated the risk by limiting permissions to the storage buckets needed for that specific role.  

Next steps

Attack path simulation capabilities are planned for availability later this summer.  We expect forthcoming enhancements to use Security AI Workbench to translate complex attack graphs to human-readable explanations of attack exposure, including impacted assets and recommended mitigations.

You can learn more about Nordnet Bank’s experience using attack path simulation at our Security Summit session. To get started with Security Command Center today, please go to the Google Cloud console.