Blog: Confidential Computing has Become the Backbone of Secure AI

In the era of advanced AI and large-scale data processing, security can no longer be an afterthought. Confidential computing has quietly become one of the most important—but often misunderstood—advances in cloud and data security. It offers a solution to a problem that most companies don’t even realize they have: how to protect sensitive data and models while they’re in use.

In this article, we’ll break down what confidential computing is, why it matters, and how tools like Corvex Confidential are making it simple for AI-driven organizations to adopt it.

What Is Confidential Computing?

Confidential computing is a technology that protects data while it’s being processed, not just while it’s stored or in transit. Using a combination of hardware-enforced security and software isolation, it creates trusted execution environments (TEEs)—also known as enclaves—within both CPUs and GPUs. These enclaves ensure that even the operating system or hypervisor cannot access the data inside.

For AI workloads, this is transformative. Whether you're training large models on regulated data or deploying proprietary inference APIs, confidential computing ensures that the data and models remain encrypted and protected from end to end.

There’s a Hidden Vulnerability in Modern AI

Today’s cloud security practices often focus on encrypting data at rest (when stored) or in transit (while moving). But there’s a hidden vulnerability: at some point, that data must be decrypted to be processed—and that's where risk creeps in whether it’s from hackers, misconfigurations, or even malicious insiders.

Once decrypted, sensitive data sits in system memory, where it can be accessed by the operating system, hypervisor, or malicious insiders. For companies working in healthcare, finance, defense, or even proprietary AI model development, this is a serious exposure.

Confidential Computing: The Nuts and Bolts

The most important part of confidential computing is the concept of a secure enclave. These secure enclaves are tamper-proof “boxes” for both the CPU and the GPU, where data remains encrypted even while it's being used. Enclaves have four defining security properties that enable them to do this.

• Isolation. The enclave is strongly isolated from the entire system. Nothing other than the CPU can look inside of an enclave, not the operating system, not the hypervisor or any other part of the system. 
• Runtime memory encryption. Everything in the enclave is always encrypted in memory. 
• The enclave is sealed. The state of the enclave is stored and restored on an otherwise untrusted system. 
• Remote attestation. Possibly the most interesting and exciting parts of confidential computing is remote attestation. The Enclave has the ability to convince a remote party that it is secure and running on secure hardware. 

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Confidential computing extends beyond CPUs into the GPU-accelerated infrastructure that powers modern AI. NVIDIA’s data center-class GPUs—such as the H200, B200, and GB200 NVL72—support Confidential Virtual Machines (CVMs) that are purpose-built for secure AI workloads. When paired with a CPU that supports hardware-based enclaves (such as Intel SGX or AMD SEV-SNP), these systems enable end-to-end data protection—from CPU to GPU memory—using hardware-enforced memory isolation.

On the GPU side, security features like secure boot, firmware integrity verification, and encrypted memory ensure that model weights, activations, and intermediate data remain protected throughout both training and inference. Combined with NVIDIA’s confidential computing software stack—including tools like Coco (confidential containers)—this architecture allows sensitive AI workloads to run in untrusted or shared environments without exposing data or proprietary models.

The result is a secure, high-performance AI pipeline where CPUs and GPUs collaborate seamlessly to enforce data privacy—making it possible to deploy advanced models while meeting the most stringent regulatory and IP protection requirements.

Who Needs Confidential Computing?

Confidential computing is critical for any organization that works with regulated data (HIPAA, GDPR, FedRAMP, PCI) to develop and deploy proprietary AI models, process sensitive user inputs in generative AI applications, or requires zero-trust cloud infrastructure where the provider itself cannot see your data. 

It’s no longer a nice-to-have, it’s an absolute requirement. For anyone building or scaling AI services in healthcare, finance, defense, enterprise SaaS, or consumer privacy-focused applications—confidential computing is the best way to enhance compliance and security.

Corvex Confidential: the Solution for Secure AI

At Corvex, we’ve eliminated the complexity traditionally associated with confidential computing. We offer pre-configured infrastructure to support secure, enclave-based AI workloads out of the box – at scale – so our developer customers don’t have to struggle with kernel patches, firmware checks, or compliance blind spots. 

We make it possible to:

• Run proprietary models securely, even in shared environments
  
  
• Process sensitive prompts and datasets without exposure
  
  
• Ensure end-to-end confidentiality across the AI pipeline—from training to inference.
  
  

We’re already running confidential computing workloads across our cluster today, including for customers in regulated industries and those with high-value AI models that must remain protected.

The Future Is Secure AI

With confidential computing, AI workloads can now encrypt model weights before deployment, stream sensitive prompts and datasets securely to GPU-accelerated enclaves, and optimize performance while ensuring privacy across every stage of the pipeline.

At Corvex, we believe this is the future of secure AI—and it’s here now. We're here to help you understand, deploy, and scale confidential computing.

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