Confidential VMs can protect the confidentiality of data in the cloud by encrypting data-in-use while it’s being processed. Confidential VMs take advantage of security technologies offered by modern CPUs from AMD, Intel, and others. With Confidential Computing, customers can be confident that their data will stay private and encrypted even while being processed in the cloud. For AI/ML workloads on Intel, we also leverage Intel AMX, a CPU accelerator that is on by default on the general purpose C3 machine series for Confidential VMs. Confidential VMs on the C3 machine series brings hardware-level protection to your AI models and data and a significant performance boost for deep learning and inference workloads.

Confidential VMs on the accelerator-optimized A3 machine series with NVIDIA H100 GPUs can empower businesses to unlock the full potential of AI and machine learning while safeguarding sensitive data. Confidential VMs with H100 GPUs help ensure data remains protected throughout the entire processing pipeline, from the moment it enters the GPU to the moment the results are generated. This reduces the risk of unauthorized access, even by privileged users or malicious actors within the system. By providing a trusted execution environment for AI workloads, Confidential VMs on the A3 machine series allows businesses to collaborate more freely and securely with partners and third-party vendors. 

With Confidential GKE Nodes, you can achieve encryption in-use for data processed inside your GKE cluster, without significant performance degradation. Confidential GKE Nodes are built on the same technology foundation as Confidential VMs. 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 or other nodes running on the host.

With Confidential Space Trusted Execution Environment (TEE), customers can harden their services by isolating them from their own insiders and privileged admins. They can also provide code integrity guarantees. This enables organizations to perform joint data analysis and machine learning (ML) model training with trust guarantees that the data they own stays protected from all parties—including the underlying infrastructure and their own operators. Learn how customers like Duality, Self, TransUnion, Symphony, Inabit, and Sertn use Confidential Space.

Dataflow is a fully managed service that supports a wide range of streaming analytics and machine learning use cases at massive scale. With Confidential VMs support in Dataflow, you can process your data pipelines using Compute Engine Confidential VMs, which provide inline memory encryption.

Dataproc enables big data processing through fully managed Spark, Hadoop, and other open source tools and frameworks. With Confidential Dataproc you can create a Dataproc cluster that uses Compute Engine Confidential VMs to provide inline memory encryption. This furthers security guarantees, especially when processing highly sensitive data.

Google Cloud Basics

Learn more about Confidential VMs, including support for end-to-end encryption, compute-heavy workloads, and more security and privacy features.

Quickstart

Quickly get up and running with a new Confidential VM instance using default settings in the Google Cloud Console.

Quickstart

Learn how to create a Confidential VM instance in an interactive tutorial.

Google Cloud Basics

Learn how to remotely verify the trustworthiness of all Google confidential environments.

Quickstart

Get started with Confidential GKE Nodes

Tutorial

Google Cloud Basics

Learn about Google Cloud’s managed Trusted Execution Environment (TEE) solution.