Introducing Confidential Computing On Cohere Model Vault

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Oct 22, 2026

4 minute read

Introducing Confidential Computing on Cohere Model Vault Now in public beta with NVIDIA Confidential Computing with NVIDIA GPUs on Microsoft Azure, bringing hardware-backed protection and verifiable security to sensitive enterprise AI.

Key takeaways Protect data in use: Model Vault extends protection beyond data at rest and in transit to prompts, documents, responses, and model execution while inference is running. Encrypt sensitive request contents end-to-end: Application payloads remain encrypted through intermediary infrastructure and can only be decrypted inside the attested confidential environment. Verify before you trust: Customers can cryptographically verify the hardware and software environment before sensitive data leaves their infrastructure. Use confidential AI as a managed service: Cohere operates the serving platform on Microsoft Azure infrastructure with NVIDIA H100 GPU Confidential Computing capabilities. Start today in public beta: Customers can start deploying encrypted Model Vault workloads today.

Today, we are announcing the public beta of Confidential Computing on Cohere Model Vault , bringing hardware-backed protection and independently verifiable security to managed enterprise AI.

Model Vault gives enterprises dedicated, fully managed model inference. With Confidential Computing, we are extending that experience to some of an organization's most sensitive workloads and data, including financial information, clinical records, privileged documents, proprietary source code, and intellectual property.

The goal is simple: enterprises should be able to use the capabilities and operational simplicity of managed AI without giving the infrastructure that operates it access to their sensitive plaintext data.

Protecting data in use Encryption traditionally protects data in two places: while it is stored and while it travels across a network.

Confidential Computing on Model Vault is designed to protect the third state: data in use .

Inference runs within a hardware-backed confidential environment spanning the CPU and GPU. Hardware-enforced isolation protects workload memory from everything outside the confidential boundary, including privileged infrastructure software and operators. With NVIDIA Confidential Computing, GPU memory is protected and traffic between the CPU and GPU is encrypted as the model performs inference.

This enables enterprises to bring more sensitive workloads to AI while reducing the number of parties and systems they need to trust with their data.

Verify before you trust

Protection alone is not enough. Enterprises also need a way to know that those protections are actually in place.

That is why remote attestation is a core part of Confidential Computing on Model Vault.

Before sensitive data is sent, customer-side software verifies cryptographically signed evidence about the environment that will process it, including its hardware, firmware, and software configuration. That evidence is checked against an approved security policy.

If the environment does not satisfy the policy, the client refuses to send the request.

This changes the trust model from "trust us" to "verify it yourself."

“Enterprises should not have to choose between the simplicity of managed AI and control over their most sensitive information. With Model Vault and Confidential Computing, customers can verify the environment that will process their data before they send it. With hardware attestation, customers not only have cryptographic proof that workloads are running in a secure, confidential computing environment, they also can verify that their data is protected from unauthorized access, even from Cohere.”

• Phil Blunsom, CTO at Cohere

End-to-end protection for sensitive request contents Confidential Computing protects what happens inside the compute environment. Model Vault also protects sensitive request contents on their way there.

Rather than relying only on connection-level encryption, Model Vault encrypts application payloads end-to-end to the attested confidential environment. The encrypted request can pass through intermediary infrastructure without exposing its contents, and only the confidential environment can decrypt it. Responses are protected on the return path as well.

This preserves the operational advantages of a managed AI platform without exposing the...