Cloud

Privacy-protecting Confidential Computing has come a long way since we introduced Confidential Virtual Machines (VMs) five years ago. The technology, which can protect data while in use, strengthens a security gap beyond data encryption at rest and in transit. 

Since then, customers have used Confidential Computing to protect patient medical data, comply with privacy guidance of GDPR and Schrems II for U.S.-Europe data transfers, and run high-performance computing (HPC) workloads securely. 

By isolating workloads in hardware-based Trusted Execution Environments (TEEs), Confidential Computing empowers customers to process their most sensitive information in the public cloud with assurance. 

As part of the advancements we’ve made with Confidential Computing, we added even more security capabilities with the introduction of Confidential VMs with Intel Trust Domain Extensions (TDX) last year. Intel TDX creates an isolated trust domain (TD) in a VM, uses hardware extensions for managing and encrypting memory to protect cloud workloads, and offers hardware-based remote attestation for verification. 

Today, we are excited to highlight our greatly expanded, and generally available, Intel TDX-based offerings, which includes Confidential GKE Nodes, Confidential Space, Confidential GPU, and more regions and zones where customers can use Confidential Computing.

Click to create a Confidential VM

Google Cloud Console now offers Google Compute Engine (GCE) customers a new interface for Intel TDX — no code changes required. To get started, follow these steps:

1. Start at the GCE Create an instance page

2. Go to the Security tab and under Confidential VM service, click Enable

3. Then select Intel TDX from the dropdown menu and click Confirm. 

It’s that simple to create a Confidential VM.

Get Confidential Computing in more regions and zones

Confidential VMs with Intel TDX were first available with support for three regions (and nine zones.) To accommodate growing demand, we’ve expanded support for Intel TDX on the C3 machine series to 10 regions (and 21 zones,) and we are planning more for the future. The full list is available here. As regional availability and scalability are critical, your account team is available to help you plan early to ensure your capacity needs are met. 

Confidential GKE Nodes with Intel TDX, now generally available

Confidential GKE Nodes are built on top of Confidential VM and deliver hardware-based protections to your Google Kubernetes Engine (GKE) clusters and node pools to ensure that your containerized workloads remain encrypted in memory. Today, Confidential GKE Nodes are generally available with Intel TDX on GKE Standard and GKE Autopilot. 

Confidential GKE Nodes with Intel TDX on the C3 machine series can be created on GKE Standard via CLI, API, UI, and Terraform. The confidential setting can be set at the cluster level or the node pool level with no code changes. You can learn more here.

Confidential GKE Nodes with Intel TDX on the C3 machine series can also be created on GKE Autopilot. It can be enabled through the use of custom compute classes. In GKE, a compute class is a profile that consists of a set of node attributes that GKE uses to provision the nodes that run your workloads during autoscaling events. Check out our documentation to get started.

Confidential Space with Intel TDX, now generally available

Also built on Confidential VM, our Confidential Space offering is a robust solution for many common issues including addressing insider threats, enabling joint machine-learning training and private gen AI inference, and fostering multi-party collaboration on sensitive data. Here are just a few examples of what our customers have built with Confidential Space:

• Confidential matching enabled customers to securely connect their first-party data for Google Ads measurement and audience solutions.

• Symphony demonstrated with its Confidential Cloud how SaaS companies can guarantee isolation of customer data from privileged insiders in the highly regulated financial industry.

• Duality delivered privacy-preserving federated learning solutions for a broad range of use cases in healthcare, financial services, and the public sector.

• Flare spearheaded innovation in verifiable AI on blockchain.

Previously, Confidential Space was only available with AMD-based technology and hardware (on the N2D, C2D, C3D, and C4D machine series), but now it is also available with Intel-based technology and hardware. This is ideal for those wanting attestation guarantees with a hardware root of trust and for those focused on Intel’s C3 machine series. 

Additionally, Confidential Space with Intel TDX is measured into runtime measurement registers (RTMR) and the measurements are verified by Google Cloud Attestation. Note that for Confidential VMs with Intel TDX, RTMRs are now populated as well. Confidential Space benefits are highlighted in the NCC Group’s latest independent security evaluation. 

Confidential VM and Confidential GKE Nodes with NVIDIA H100 GPUs, now generally available

If you’re looking for performance and security while protecting data in use, Confidential VM and Confidential GKE Nodes with NVIDIA H100 GPUs on the accelerator-optimized A3 machine series are now generally available. These offerings deliver Google Cloud’s first Confidential GPUs, focus on ease of use to meet the demand for secure computing, and extend security to data-intensive, AI and ML workloads by having Intel TDX enabled on the CPU and NVIDIA Confidential Computing enabled on the GPU. You now have the ability to secure your data performantly during inference and training across models.

Confidential VM with NVIDIA H100 GPUs is available with the a3-highgpu-1g machine type and in three zones: europe-west4-c, us-central1-a, and us-east5-a. No code changes are needed for most AI and ML workloads. For pricing details, see here. Confidential GKE Nodes with NVIDIA H100 GPUs are generally available on both GKE Standard and GKE Autopilot (through custom compute class). To get started, click here. 

And, we also have Confidential Space with NVIDIA H100 GPUs in preview.

Intel has a free tier for independent attestation

Intel’s attestation verifier service, Intel Tiber Trust Authority, now has a free tier. Google Cloud Confidential VMs and Confidential Space are both integrated with Intel Tiber Trust Authority as a third party attestation service, but now Intel Tiber Trust Authority is making secure attestation more accessible for all by offering a free tier (with optional paid support). 

When Confidential VM and Confidential Space customers use Intel Tiber Trust Authority, they can gain stronger separation of duties security guarantees. Click here to learn more. 

What our customers say

“Thanks to the joint efforts of Super Protocol, Google Cloud, and NVIDIA, the world now gains a new layer of possibility — unlocking Confidential AI without cloud borders. With A3 Confidential VMs built on NVIDIA H100 GPUs now integrated into Super’s decentralized infrastructure and marketplace, companies can securely run, monetize, and collaborate on sensitive AI and data — across any environment. This enables seamless collaboration between Google Cloud customers and partners in other clouds — with no need for shared trust, manual agreements, or compromise. For the broader market, A3 instances at scale accelerate global access, while Super ensures confidentiality, verifiability, and self-sovereignty — fully automated and requiring no expertise in confidential computing. We are excited to open this next chapter of Confidential AI, built to work wherever you and your partners are,” said Nukri Basharuli, founder and CEO, Super Protocol.

“We’re proud to have partnered with Google Cloud to validate their Confidential Computing-enabled GPU solution — a major step forward in securing sensitive data for AI and machine learning workloads, without compromising on performance or scalability. Confidential Computing allows organizations to process sensitive workloads in the cloud while protecting sensitive data and models from both the cloud provider and the organization’s insiders and internal threats. However, for gen AI and agentic AI use cases, protecting the CPU alone isn’t enough — both CPU and GPU must also run in confidential mode with mutual trust. With Google Cloud’s new offering, Anjuna can now launch Confidential Containers that leverage Intel TDX and NVIDIA H100 GPUs in confidential mode. This ensures that data, configurations, secrets, and code remain protected end-to-end from any untrusted entity, bringing state-of-the-art security for sensitive data.” said Steve Van Lare, CTO, Anjuna Security.

“With data processing worldwide growing up to three times faster than ever before and doubling every six months, the future of cloud computing must be built on trust. In collaboration with Google, Modelyo leverages Confidential VMs on the A3 machine series with NVIDIA H100 GPUs, transforming Confidential Computing into a seamless, intuitive, and fully integrated cloud experience. This enables us to deliver end-to-end managed solutions across interconnected environments, empowering organizations to innovate confidently knowing their data remains effortlessly protected at every stage.” said Benny Meir, CEO, Modelyo.

How to get started with Confidential Computing

To add that extra layer of protection and privacy to your sensitive workloads, check out our documentation for Confidential VMs and Confidential GKE Nodes today.

AI is transforming data into a strategic asset, driving demand for flexible, integrated, and real-time data architectures. But yesterday’s data tools can’t handle AI’s demand for massive volumes of real-time and multi-modal data. Data lakes, for instance, offer flexibility for raw data but lack enforcement and consistency. Meanwhile, traditional data marts, warehouses, and lake architectures often result in silos, and require costly ETL to bridge analytical, unstructured, and operational data.

The shift to open lakehouses relies on open table formats like Apache Iceberg, which has emerged as the de facto open-source table format for data lakes. Today, alongside our partners Confluent, Databricks, dbt, Fivetran, Informatica and Snowflake, we’re excited to reiterate our commitment to this open standard. Whether you’re integrating best-of-breed services from diverse providers or navigating a complex data landscape because of a merger and acquisition, adopting an open table format like Iceberg can help you dismantle your traditional data silos.

United in our Iceberg support

At its core, Iceberg provides a metadata layer that enables efficient query planning and data management. This crucial layer, encompassing table schema, partitioning, and data file locations, powers advanced features like time travel and data pruning, which allows data teams to swiftly pinpoint relevant data, streamline performance, and accelerate insights.

The data management industry is coalescing around the open Apache Iceberg standard. At Google Cloud, we recently delivered innovations which leverage Google’s Cloud Storage (GCS) to provide an enterprise-grade experience for managing and interoperating with Iceberg data, including BigLake tables for Apache Iceberg and BigLake Metastore with a new REST Catalog API.  Databricks recently announced Iceberg support with their Unity Catalog, allowing users to read and write managed Iceberg tables across a variety of catalogs. Similarly, Snowflake supports interoperable storage with Apache Iceberg tables, allowing organizations to access Iceberg data within Snowflake, minimizing the latency associated with ingesting or copying data.

“This open, standard interface allows any Iceberg-compatible engine — including BigQuery, Apache Spark, Databricks, and Snowflake — to operate on the same, single copy of Iceberg data. This powerful architecture even bridges the gap between analytical and operational workloads. By supporting Iceberg and other open table formats in Unity Catalog, we’re unifying data and governance across the enterprise to truly democratize data and AI. No matter what table format our customers choose, we ensure it’s accessible, optimized, and governed for Business and Technical users.” – Ryan Blue, Original creator of Apache Iceberg, Member of Technical Staff, Databricks

“Customers shouldn’t have to choose between open formats and best-in-class performance or business continuity. Snowflake’s native support for open source standards unifies data while preserving flexibility and choice, paving the way to build and securely scale high-performing lakehouses without silos or operational overhead.” – Rithesh Makkena, Senior Regional Vice President of Partner Solutions Engineering, Snowflake

At Google Cloud, we’re committed to an open Data Cloud that lets data teams build modern, data-driven applications wherever their workloads are, while using open source, open standards and open formats like Apache Iceberg. 

We partner closely with an extensive ecosystem of partners including Confluent, dbt Labs, Fivetran, and Informatica on Apache Iceberg initiatives.

“Apache Iceberg has emerged as a critical enabler for the open data ecosystem, providing the flexibility, interoperability, and consistency that modern, real-time data architectures demand. At Confluent, we’re dedicated to helping customers leverage this power. Our Tableflow innovation, by representing Apache Kafka topics as open Iceberg tables, exemplifies how this open format eliminates complex ETL and ensures data is always fresh, accurate, and instantly actionable for critical real-time analytics and AI.” – Shaun Clowes, Chief Product Officer, Confluent

“dbt was born out of an open source project to help people transform data. Open data ecosystems are at the core of what we do. Supporting Iceberg in dbt ensures that our customers will have standards and choices for how they use their transformed data in their AI and data workflows.” – Ryan Segar, Chief Product Office, dbt Labs

“Open table formats like Apache Iceberg make it possible to reduce data copies by decoupling your data from the compute engines used to access it. Fivetran’s Managed Data Lake service ensures data is delivered to cloud storage as transactionally consistent tables in a way that preserves the structure from the source. Fivetran’s Managed Data Lake seamlessly integrates with Google Cloud Storage and BigLake metastore, providing a single governance layer within customers’ Google projects and making Iceberg tables just as easy to query as native BigQuery tables.” – Dan Lynn, Vice President of Product Management, Databases, Fivetran

“Our collaboration with Google on the Iceberg format is ushering in a new era of open, interoperable data architecture. Together, we’re enabling organizations to unify their data effortlessly, accelerate insights and innovate without limits by eliminating silos and unlocking the full power of the modern data ecosystem.” – Rik Tamm-Daniels, GVP, Technology Alliances

The power of shared data

By adopting Iceberg, customers can share data across different query engines and platforms, leveraging shared datasets for a multitude of workloads and improving interoperability. Organizations can now share data from Snowflake to BigQuery, unlocking powerful BigQuery ML capabilities such as text generation or machine translation, and simplifying ML model development and deployment. Likewise, data teams can share data with BigQuery from Databricks to achieve cost efficiencies, leverage built-in ML, or implement agentic workflows.

Customers like Global Payments embraced Iceberg for more flexibility across their diverse data tools. BigQuery and Snowflake serve millions of merchants, and allow the business to analyze transaction data and unlock deep customer insights.

Likewise, Unilever has transformed its data management approach with Iceberg, which allows it to manage large datasets more efficiently, particularly in a data lakehouse architecture. Using a combination of Google Cloud and Databricks, Unilever stores and analyzes large amounts of complex data, allowing them and their suppliers to take action wherever and whenever needed. 

Whether you create your Iceberg tables in BigQuery, Databricks, or Snowflake, you can leverage the resulting data from any platform and have your tables stay continuously up-to-date. This interoperability will help you operate with greater efficiency and security, drastically reducing the time you spend moving or duplicating datasets, and eliminating the need for complex pipelines to utilize your preferred tools, platforms, and processing systems.

Get started today with BigQuery and BigLake for your AI-ready lakehouse. You can learn how to build an open data lakehouse with BigQuery and Snowflake by watching a tutorial, then diving into the Quickstart Guide. Learn how to connect and build an open data lakehouse with BigQuery and Databricks.

Modern application development requires organizations to invest not only in scale but also in simplification and central governance. This means more than message routing; it requires a simple, unified messaging platform that can intelligently filter, transform, and govern the flow of information in real-time, taming complexity all in one place.

Today, we are excited to announce the general availability of Eventarc Advanced, a unified, serverless eventing platform that goes beyond simple routing by combining real-time filtering, transformation, management, and delivery in one place — for a complex, multi-source event-driven architecture.

Evolving Eventarc to handle complexity

Eventarc Advanced is an evolution of Eventarc Standard and offers out-of-the-box integration patterns to simplify your eventing needs.

With Eventarc Advanced, organizations can

• Integrate existing services using Publish API and leverage Google Cloud events to build sophisticated event-driven applications.

• Centrally manage, secure, and observe the flow of messages across services with support for per-message fine-grained access control.

• Intelligently route messages to appropriate destinations based on flexible message criteria.

• Transform and convert events in real-time, with support for multiple payload formats and built-in capability to transform event attributes.

• Publish to Google Cloud services using HTTP binding.

With Eventarc Advanced, you can build sophisticated eventing systems. In contrast, Eventarc Standard is best for simple one-to-one eventing needs involving Google Cloud events (comparison).

Eventarc Advanced’s key technical features include:

• Publish API to ingest custom and third-party messages using CloudEvents format (details).

• Message bus that acts as the central nervous system of your event-driven architecture, providing centralized observability, security and management. Message bus is based on Envoy and uses the policy engine of Cloud Load Balancers and Cloud Service Mesh.

• Your existing systems can publish messages to a central message bus that can be intelligently routed to appropriate consumers based on flexible criteria. The message bus simplifies event management and reduces operational overhead. 

• You can gain insights into your message flows with centralized monitoring, logging, and tracing capabilities. Logs are captured in Cloud Logging, providing detailed information about event processing and errors.

• Eventarc Advanced incorporates error-handling by offering reliable event delivery and graceful recovery from transient failures.

Empowering developers and operators

We designed Eventarc Advanced to cater to the needs of both developers and operators:

• “Simplicity” for developers: Focus on building your core application features, not on complex event routing logic. Eventarc Advanced provides a unified API and a consistent experience, letting you build decoupled, reliable, and scalable services including real-time transformations.

• “Centralized governance” for platform operators: Simplify the setup and management of your eventing infrastructure. Centralized governance across projects / teams, plus monitoring and logging make it easier to identify and resolve issues, reducing operational overhead. 

How Eventarc Advanced works

Imagine an order processing system where orders are created, payments are processed, and items are shipped. Each action is an “event,” and in a complex system, managing this flow can be challenging. This is where Eventarc Advanced comes in. It provides a centralized way to manage, observe, and route all your application’s events. Let’s explore how it works.

Set up your message bus
At the heart of Eventarc Advanced is a message bus that acts as the central nervous system for your event-driven application. Every event, regardless of its origin, is sent to the message bus to be analyzed and routed. This central hub is where you can define security policies, controlling exactly who can send events and what kind are allowed.

In our example, you would create a message bus to receive all order-related events. Whether an order is newly created, its payment is confirmed, or its status changes to “shipped,” the events land here.

Connect your event sources
Next, connect your sources that generate order events. Event sources are the services and applications that generate events and feed them into your message bus. Eventarc Advanced makes this easy, supporting a wide range of sources, including:

• Google API events

• External apps or custom systems via Publish API

In our example, the event source could be a custom service using the Publish API. Every time a new order is saved or an existing one is updated, it automatically sends an event to your message bus.

Configure pipelines and destinations
This is another area where Eventarc Advanced shines. With events flowing into your message bus, you can configure pipelines to intelligently route them to the correct destinations, allowing you to filter, transform, and direct events with precision.

In the above example,

• New order notification: You can set up a filter that looks for events with status: “new”. This pipeline routes these events to a notification service that sends an order confirmation email to the customer.

• Fraud detection: For high-value orders (e.g., amount > $1000), you can apply a transformation and route it to a specialized fraud detection service for analysis.

Unlocking new possibilities

Eventarc Advanced opens up new possibilities for your applications and workflows:

• Large-scale application integration: Connect numerous services and agents, enabling them to communicate asynchronously and reliably, even across different event formats and schemas.

• Event streaming for AI and analytics: Handle the influx of data from IoT devices and AI workloads by filtering and transforming them before feeding them into your analytics pipelines.

• Hybrid and multi-cloud deployments: Extend your event-driven architectures beyond Google Cloud, integrating with on-premises systems and other cloud providers. 

What’s next

As today’s applications become increasingly agentic, distributed and data-driven, the need for efficient and secure event orchestration is more critical than ever. With upcoming native support for Service Extensions to insert custom code into the data path and services like Model Armor, Eventarc Advanced’s message bus provides security and networking controls for agent communications. 

Eventarc Advanced is available today. To learn more about Eventarc Advanced, see the documentation. To learn more about event-driven architectures, visit our Architecture Center based on Google Cloud best practices. Get ready to take your event-driven architectures to the next level!