Cloud

Many organizations in regulated industries and the public sector that want to start using generative AI face significant challenges in adopting cloud-based AI solutions due to stringent regulatory mandates, sovereignty requirements, the need for low-latency processing, and the sheer scale of their on-premises data. Together, these can all present institutional blockers to AI adoption, and force difficult choices between using advanced AI capabilities and adhering to operational and compliance frameworks.

We are announcing Google Distributed Cloud (GDC) Sandbox – AI Optimized, which offers a virtualized platform that mirrors the GDC air-gapped racks and appliance experience, allowing developers to innovate on new apps with gen AI capabilities, and it is now generally available.  

GDC Sandbox can help organizations harness Google’s gen AI technologies while maintaining control over data, meeting rigorous regulatory obligations, and unlocking a new era of on-premises AI-driven innovation. With flexible deployment models, a robust security architecture, and transformative AI applications like Google Agentspace search, GDC Sandbox enables organizations to accelerate innovation, enhance security, and realize the full potential of AI.

Secure development in isolated environments 

For sovereign entities and regulated industries, a secure Zero Trust architecture via platforms like GDC Sandbox is a prerequisite for leveraging advanced AI. GDC Sandbox lets organizations implement powerful use cases — from agentic automation and secure data analysis to compliant interactions — while upholding sovereign Zero Trust mandates for security and compliance. 

“GDC Sandbox provides Elastic with a unique opportunity to enable air-gapped gen AI app development with Elasticsearch, as well as enable customers to rapidly deploy our Security Incident & Event Management (SIEM) capabilities.” – Ken Exner, Chief Product Officer, Elastic

“Accenture is excited to offer Google Distributed Cloud air-gapped to customers worldwide as a unique solution for highly secure workloads. By using GDC Sandbox, an emulator for air-gapped workloads, we can expedite technical reviews, enabling end-customers to see their workloads running in GDC without the need for lengthy proofs of concept on dedicated hardware.” – Praveen Gorur, Managing Director, Accenture 

Air-gapped environments are challenging

Public sector agencies, financial institutions, and other organizations that handle sensitive, secret, and top-secret data are intentionally isolated (air-gapped) from the public internet to enhance security. This physical separation prevents cyberattacks and unauthorized data access from external networks, helping to create a secure environment for critical operations and highly confidential information. However, this isolation significantly hinders the development and testing of cutting-edge technologies. Traditional air-gapped development often requires complex hardware setups, lengthy procurement cycles, and limits access to the latest tools and frameworks. These limitations hinder the rapid iteration cycles essential to development.

According to Gartner® analyst Michael Brown in the recent report U.S. Federal Government Context: Magic Quadrant for Strategic Cloud Platform Services, where Google Cloud is evaluated as a Notable Vendor, “Federal CIOs will need to consider cost and feature availability in selecting a GCC [government community cloud] provider. Careful review of available services within the compliance scope is necessary. A common pitfall is the use of commercially available services in early solution development and subsequently finding that some of those services are not available in the target government community environment. This creates technical debt requiring refactoring, which results in delays and additional expense.”

GDC Sandbox: A virtualized air-gapped environment

GDC Sandbox addresses these challenges head-on. This virtual environment emulates the experience of GDC air-gapped, allowing you to build, test, and deploy gen AI applications using popular development tools and CI/CD pipelines. With it, you don’t need to procure hardware or set up air-gapped infrastructure to test applications with stringent security requirements before moving them to production. Customers can leverage Vertex AI APIs for key integrations with GDC Sandbox – AI Optimized including:

• Google AI Studio: Access Vertex APIs

• Optical character recognition (OCR): Extract text from images and documents

• Speech-to-text: Convert spoken language into written text

• Translation: Break down language barriers for multilingual applications

• Containerized model hosting: Deploy and manage custom gen AI models within containers

• GPUs: Dedicate user-space GPUs for gen AI development

One of the things that sets GDC Sandbox apart is its consistent user interface. As seen above, developers familiar with Google Cloud will find themselves in a comfortable and familiar environment, which helps streamline the development process and reduces the learning curve. This means you can jump right into building and testing your gen AI applications without missing a beat.

“GDC Sandbox has proven to be an invaluable tool to develop and test our solutions for highly regulated customers who are looking to bring their air-gapped infrastructures into the cloud age.” – David Olivier, Defense and Homeland Security Director, Sopra Steria Group

“GDC Sandbox provides a secure playground for public sector customers and other regulated industries to prototype and test how Google Cloud and AI can solve their unique challenges. By ensuring consistency with other forms of compute, we simplify development and deployment, making it easier for our customers to bring their ideas to life. We’re excited to see how our customers use the GDC Sandbox to push the boundaries of what’s possible.” – Will Grannis, VP & CTO, Google Cloud

The GDC Sandbox architecture and experience

GDC Sandbox offers developers a familiar and intuitive environment by mirroring the API, UI, and CLI experience of GDC air-gapped and GDC air-gapped appliance. It offers a comprehensive suite of services, including virtual machines, Kubernetes clusters, storage, observability, and identity management. This allows developers to build and deploy a wide range of gen AI applications, and leverage the power of Google’s AI and machine learning expertise within a secure, dedicated environment.

Use cases for GDC Sandbox 

GDC Sandbox offers numerous benefits for organizations with air-gapped environments. Let’s explore some compelling use cases:

• Gen AI development: Develop and test Vertex and gen AI applications via GPUs to cost-effectively validate them in secure production environments.

• Partner enablement: Empower partners to build applications, host GDC Marketplace offerings, train personnel, and prepare services for production. 

• Training and proof of concepts: Provide hands-on training for developers and engineers on GDC air-gapped technologies and best practices. Deliver ground-breaking new capabilities and showcase the art of the possible for customers and partners. 

Building applications in GDC Sandbox

GDC Sandbox leverages containers and Kubernetes to host your applications. To get your application up and running, follow these steps:

1. Build and push: Build your application image locally using Docker and ensure your Dockerfile includes all necessary dependencies. Tag your image in your source repository then sync with the Harbor instance URI and push it to the provided Harbor repository.

2. Deploy with Kubernetes: Create a Kubernetes deployment YAML file that defines your application’s specifications, including the Harbor image URI and the necessary credentials to access the image. Apply this file using the kubectl command-line tool to deploy your application to the Kubernetes cluster within the Sandbox.

3. Expose and access: Create a Kubernetes service to expose your application within the air-gap. Retrieve the service’s external IP using kubectl get svc to access your application.

4. Migrate and port: Move your solutions from GDC Sandbox to GDC air-gapped and appliance deployments. 

Ready to try GDC Sandbox?

Watch our on-demand video and getting started demo to learn more about GDC Sandbox capabilities and benefits. If you would like to discuss how to get access to GDC Sandbox please complete this form, and a member of our team will be in touch.

^{U.S. Federal Government Context: Magic Quadrant for Strategic Cloud Platform Services, By Michael Brown, 3 February 2025}

^{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.}

As the first fully cloud-native private bank in Switzerland, Alpian stands at the forefront of digital innovation in the financial services sector. With its unique model blending personal wealth management and digital convenience, Alpian offers clients a seamless, high-value banking experience. 

Through its digital-first approach built on the cloud, Alpian has achieved unprecedented agility, scalability, and compliance capabilities, setting a new standard for private banking in the 21st century. In particular, its use of generative AI gives us a glimpse of the future of banking.

The Challenge: Innovating in a Tightly Regulated Environment

The financial industry is one of the most regulated sectors in the world, and Switzerland’s banking system is no exception. Alpian faced a dual challenge: balancing the need for innovation to provide cutting-edge services while adhering to stringent compliance standards set by the Swiss Financial Market Supervisory Authority (FINMA).

Especially when it came to deploying a new technology like generative AI, the teams at Alpian and Google Cloud knew there was virtually no room for error.

Tools like Gemini have streamlined traditionally complex processes, allowing developers to interact with infrastructure through simple conversational commands. For instance, instead of navigating through multiple repositories and manual configurations, developers can now deploy a new service by simply typing their request into a chat interface.

This approach not only accelerates deployment times — reducing them from days to mere hours — it’s also empowered teams to focus on innovative rather than repetitive tasks. 

There are limits, to be sure, both to ensure security and compliance, as well as focus on the part of teams.

Thanks to this platform with generative AI, we haven’t opened the full stack to our engineers, but we have created a defined scope where they can interact with different elements of our IT using a simplified conversational interface. It’s within these boundaries that they have the ability to be autonomous and put AI to work.

Faster deployment times translate directly into better client experiences, offering quicker access to new features like tailored wealth management tools and enhanced security. This integration of generative AI has not only optimized internal workflows but also set a new benchmark for operational excellence in the banking sector.

A Collaborative Journey to Success

Alpian worked closely with its team at Google Cloud to find just the right solutions to meet it’s evolving needs. Through strong trust, dedicated support and expertise, they were able to optimize infrastructure, implement scalable solutions, and leverage AI-powered tools like Vertex AI and BigQuery.

“Google Cloud’s commitment to security, compliance, and innovation gave us the confidence to break new ground in private banking,” Damien Chambon, head of cloud at Alpian, said.

Key Results

Alpian’s cloud and AI work has already had a meaningful impact on the business:

• 25% faster feature deployment, ensuring quicker time-to-market for innovative banking products.

• Enhanced developer productivity with platform engineering, enabling more independence and creativity within teams.

• Automated compliance workflows, aligning seamlessly with FINMA’s rigorous standards.

• Simplified deployment processes, reducing infrastructure complexity with tools like Gemini

These achievements have enabled Alpian to break down traditional operational silos, empowering cross-functional teams to work in harmony while delivering customer-focused solutions.

Shaping the Future of Private Banking

Alpian’s journey is just beginning. With plans to expand its AI capabilities further, the bank is exploring how tools like machine learning and data analytics can enhance client personalization and operational efficiency. By leveraging insights from customer interactions and integrating them with AI-driven workflows, Alpian aims to refine its offerings continually and remain a leader in the competitive digital banking space.

By aligning technological advancements with regulatory requirements, Alpian is creating a model for the future of banking — one where agility, security, and customer-centricity can come together seamlessly and confidently.

As an AI/ML developer, you have a lot of decisions to make when it comes to choosing your infrastructure — even if you’re running on top of a fully managed Google Kubernetes Engine (GKE) environment. While GKE acts as the central orchestrator for your AI/ML workloads — managing compute resources, scaling your workloads, and simplifying complex workflows — you still need to choose an ML framework, your preferred compute (TPU or GPUs), a scheduler (Ray, Kueue, Slurm) and how you want to scale your workloads. By the time you have to configure storage, you’re facing decision fatigue! 

You could simply choose Google’s Cloud Storage for its size, scale and cost efficiency. However, Cloud Storage may not be a good fit for all use cases. For instance, you might benefit from a storage accelerator in front of Cloud Storage like Hyperdisk ML for better model weights load times. But in order to benefit from the acceleration these bring, you would need to develop custom workflows to orchestrate data transfer across storage systems. 

Introducing GKE Volume Populator 

GKE Volume Populator is targeted at organizations that want to store their data in one data source and let GKE orchestrate the data transfers. To achieve this, GKE leverages the Kubernetes Volume Populator feature through the same PersistentVolumeClaim API that customers use today. 

GKE Volume Populator along with the relevant CSI drivers dynamically provision a new destination storage volume and transfer data from your Cloud Storage bucket to the destination storage volume. Your workload pods then wait to be scheduled until the data transfer is complete.

Using GKE Volume Populator provides a number of benefits:

• Low management overhead: As part of a managed solution that’s enabled by default, GKE Volume Populator handles the data transfer, so you don’t need to build a bespoke solution for data hydration but leave it to GKE.

• Fine-grained access control: GKE Volume Populator supports namespace-level Cloud Storage bucket access authentication.

• Optimized resource utilization: Your workload pods are blocked for scheduling until the data transfer completes. You can use your GPUs/TPUs for other tasks while data is being transferred.

• Easy progress tracking: Monitor the data transfer progress by checking the event message on your PVC object. 

Customers like Abridge AI report that GKE Volume Populator is helping them streamline their AI development processes.

“Abridge AI is revolutionizing clinical documentation by leveraging generative AI to summarize patient-clinician conversations in real time. By adopting Google Cloud Hyperdisk ML, we’ve accelerated model loading speeds by up to 76% and reduced pod initialization times. Additionally, the new GKE Volume Populator feature has significantly streamlined access to large models and LoRA adapters stored in Cloud Storage buckets. These performance improvements enable us to process and generate clinical notes with unprecedented efficiency — especially during periods of high clinician demand.” – Taruj Goyal, Software Engineer, Abridge

Accelerate your data via Hyperdisk ML

Let’s say you have an AI/ML inference workload, and your data is stored in a Cloud Storage bucket, you want to move your data from the Cloud Storage bucket to a Hyperdisk ML instance to accelerate the loading of model weights, scale up to 2,500 concurrent nodes and reduce the pod over-provisioning. Here’s how to do this with GKE Volume Populator:

1. Prepare your GKE Cluster: Create a GKE cluster with the corresponding CSI driver, and enable Workload Identity Federation for GKE.

2. Set up necessary permissions: Configure permissions so that GKE Volume Populator has read access to your Cloud Storage bucket. 

3. Define Your data source: Create a GCPDataSource This specifies:

• The URL of the Cloud Storage bucket that contains your data
• The Kubernetes Service Account you created with read access to the bucket

4. Create your PersistentVolumeClaim: Create a PVC that refers to the GCPDataSource you created in step 3 and the corresponding StorageClass for the destination storage.

5. Deploy Your AI/ML workload: Create your inference workload with the PVC. Configure this workload to use the PVC you created in step 4. 

GKE Volume Populator is generally available, and support for Hyperdisk ML is in preview. To enable it in your console, reach out to your account team.

“There’s no red teaming on the factory floor,” isn’t an OSHA safety warning, but it should be — and for good reason. Adversarial testing in most, if not all, manufacturing production environments is prohibited because the safety and productivity risks outweigh the value. 

If resources were not a constraint, the security team would go build another factory with identical equipment and systems and use it to conduct proactive security testing. Almost always, costs outweigh the benefits, and most businesses simply can not support the expense. 

This is where digital twins can help. Digital twins are essentially IT stunt doubles, cloud-based replicas of physical systems that use real-time data to create a safe environment for security and resilience testing. The digital twin environment can be used to test for essential subsystem interactions and repercussions as the systems transition from secure states to insecure states.

Security teams can operationalize digital twins and resilience analysis using the following approach:

1. Gain a deep understanding about the correlations between the leading indicators of cyber resilience and the role of digital twins in becoming resilient. The table below offers this mapping. 

2. Get buy-in from business leaders, including the CMO, CIO, and CTO. Security teams should be able to demonstrate the strategic value to the organization by using digital twins for adversarial security testing without disrupting production. 

3. Identify the right mix of engineers and security experts, as well as appropriate technologies to execute the strategy. Google Cloud’s security and infrastructure stack is positioned to help security teams achieve operational digital twins for security (see table below).

What digital twins architecture can look like with Google Cloud

To build an effective digital twin, the physics of the electrical and mechanical systems must be represented with sufficient accuracy. 

The data needed for the construction of the twin should either come from the physical sensors or computed using mathematical representations of the physical process. The twin should be modeled across three facets: 

1. Subsystems: Modeling the subsystems of the system, and pertinent interactions between the subsystems (such as a robotic arm, its controller, and software interactions). 

2. Networks: Modeling the network of systems and pertinent interactions (such as plant-wide data flow and machine-to-machine communication). 

3. Influencers: Modeling the environmental and operational parameters, such as temperature variations, user interactions, and physical anomalies causing system and network interruptions. 

Developing digital twins in diverse OT environments requires secure data transmission, compatible data storage and processing, and digital engines using AI, physics modeling, applications, and visualization. This is where comprehensive end-to-end monitoring, detection, logging, and response processes using tools such as Google Security Operations and partner solutions comes in. 

The following outlines one potential architecture for building and deploying digital twins with Google Cloud:

• Compute Engine to replicate physical systems on a digital plane

• Cloud Storage to store data, simulate backup and recovery

• Cloud Monitoring to emulate on-prem monitoring and evaluate recovery process

• Manufacturing Data Engine (MDE) to securely transfer live data from the manufacturing/OT systems

• Cloud Pub/Sub for real-time messaging service for streaming data from systems and sensors. MDE uses Pub/Sub.

• Google Kubernetes Engine (GKE) to run failures scenarios in a modular isolated fashion

• Google Cloud VPN to simulate secure and insecure connection to the twins and simulate connectivity failure scenarios

• Network Intelligence Center to gain network performance metrics during failure and recovery scenarios

• Cloud Logging to perform retrospective analysis and perform live detection. 

• Cloud Armor to evaluate defense against simulated DDoS attacks

• Security Command Center offers two key tools: Attack Path simulation, which can emulate realistic cyberattacks in the digital twin environment; and web and vulnerability scanning to tailor the attack scenarios to simulated exploitation of existing production systems vulnerabilities.

• BigQuery to store, query, and analyze the datastreams received from MDE and to perform adversarial testing’s postpartum analysis

• Spanner Graph and partner solutions such as neo4j to build and enumerate the industrial process based on graph-based relationship modeling 

• Machine learning services (including Vertex AI, Gemini in Security, partner models through Vertex AI Model Garden) to rapidly generate relevant failure scenarios and discover opportunities of secure customized production optimization. Similarly, use Vision AI tools to enhance the digital twin environment, bringing it closer to the real-world physical environment.

• Cloud Run functions for serverless compute platform, which can run failure-event-driven code and trigger actions based on digital twin insights

• Looker to visualize and create interactive dashboards and reports based on digital twin and event data

• Apigee to securely expose and manage APIs for the digital twin environment. This allows for controlled access to real-time data from on-prem OT applications and systems. For example, Apigee can manage APIs for accessing building OT sensor data, controlling HVAC systems, and integrating with third-party applications for energy management.

• Google Distributed Cloud to run digital twins in an air-gapped, on-premises, containerized environment

Security and engineering teams can use the above Google Cloud services illustration as a foundation and customize it to their specific requirements. While building and using digital twins, both security of the twins and security by the twins are critical. To ensure that the lifecycle of the digital twins are secure, cybersecurity hardening, logging, monitoring, detection, and response should be at the core design, build, and execution processes.

This structured approach enables modelers to identify essential tools and services, define in-scope systems and their data capabilities, map communication and network routes, and determine applications needed for business and engineering functions.

Getting started with digital twins

Digital twins are a powerful tool for security teams. They help us better understand and measure cyber-physical resilience through safe application of cyber-physical resilience leading indicators. They also allow for the adversarial testing and analysis of subsystem interactions and the effects of systems moving between secure and insecure conditions without compromising safety or output.

Security teams can begin right away to use Google Cloud to build and scale digital twins for security:

1. Identify the purpose and function that security teams would like to simulate, monitor, optimize, design, and maintain for resilience.

2. Select and identify the right physical or industrial object, system, or process to be replicated as the digital twin.

3. Identify pertinent data flows, and interfaces, and dependencies for data collection and integration.

4. Be sure to understand the available IT and OT, cloud, and on-premises telemetry across the physical or industrial object,system, or process.

5. Create the virtual model that accurately represents its physical counterpart in all necessary aspects.

6. The replica should be connected to its physical counterpart to facilitate real-time data flow to the digital twin. Use a secure on-premises connector such as MDE to make the secure connection between the physical and digital environments running on Google Cloud VPC.

7. To operationalize the digital twin, build the graph-based entity relationship model using Spanner Graph and partner solutions like neo4j. This uses the live data stream from the physical system and represents it on the digital twin.

8. Use a combination of Cloud Storage and BigQuery to store discrete and continuous IT and OT data such as system measurements, states, and file dumps from the source and digital twin.

9. Discover common mode failures based on the mapped processes that include internal and external dependencies.

10. Use at least one leading indicator with Google Threat Intelligence to perform threat modeling and evaluate the impact on the digital twin model.

11. Run Google’s AI models on the digital twins to further advance the complexity of cyber-resilience studies.

12. Look for security and observability gaps. Improve model fidelity. Recreate and update the digital twin environment. Repeat step 10 with a new leading indicator, new threat intelligence, or an updated threat model.

13. Based on the security discoveries from the resilience studies on the digital twin, design and implement security controls and risk mitigations in the physical counterpart.

To learn more about how to build a digital twin, you can read this ebook chapter and contact Google Cloud’s Office of the CISO.