NVIDIA’s AI infrastructure security designs can help isolate workloads and protect model assets and sensitive data during execution, using confidential-computing hardware, attestation and policy-controlled key release. They do not secure an entire AI service automatically. The customer still has to configure and operate the platform, govern data and outputs, and secure application controls that the documented architecture leaves out.
What does NVIDIA’s security architecture protect?
Confidential computing is intended to protect data and code while they are being processed inside a defined, isolated execution environment. NVIDIA’s confidential-computing materials describe CPU and GPU capabilities for workload isolation and integrity verification. In the documented designs, remote attestation supplies evidence about the environment so a policy can decide whether it is trusted enough to receive protected secrets, such as keys used to decrypt a model.
Attestation is not a general security certificate for an AI system. It is a check against specified evidence and policy. NVIDIA’s self-hosted Kubernetes pattern calls for evidence spanning the CPU, GPU, guest environment, workload image, runtime policy and firmware state. A verifier evaluates that evidence; a key-release service should release secrets only when the evidence is fresh and matches policy. Missing or mismatched evidence should result in a denied release, not a bypass.
NVIDIA’s Confidential Containers reference architecture combines components including Kata-based sandbox isolation, GPU passthrough, composite attestation and attestation-based key release. The GPU Operator helps provision GPU support and manage GPU confidential-computing mode; Trustee provides attestation and key-brokering services in the described pattern. These components can form a security boundary when correctly configured. They do not, by themselves, establish that a particular deployment is secure or provide application authorization, guardrails, tenant isolation, network security or incident response.
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Which deployment pattern are you securing?
The responsibilities and workload scope depend on the architecture. NVIDIA’s references describe different boundaries; they should not be treated as interchangeable guarantees.
| Pattern | Documented boundary and workload | What the reference does not establish |
|---|---|---|
| Confidential Containers on Kubernetes | A confidential pod/runtime pattern with sandbox isolation, GPU integration, attestation and policy-controlled key release. See NVIDIA’s Confidential Containers Reference Architecture and self-hosted Kubernetes reference. | That a deployed cluster meets the reference requirements, or that application authorization, guardrails, network controls and response are handled automatically. |
| Self-hosted confidential VM | GPU-accelerated inference inside a confidential VM, with CPU and GPU confidential computing, remote attestation, policy-controlled key release, model-image lifecycle, network controls and operational signals. See NVIDIA’s self-hosted VM reference architecture. | The document excludes Kubernetes-native confidential containers, training and fine-tuning, fleet orchestration, and model-server authorization, guardrails and application-level multi-tenancy. |
| DGX BasePOD | An enterprise infrastructure architecture covering DGX compute, InfiniBand compute fabric, Ethernet management and storage, out-of-band management, management servers, storage partners and NVIDIA software. NVIDIA document RA-11127-001 V5 was published 2025-08-06. | A BasePOD deployment architecture is not, on its own, a definition of the confidential execution boundary or proof that customer security responsibilities are removed. |
Before applying any reference to a real system, identify its exact workload and deployment pattern. The self-hosted VM document’s scope, for example, is inference—not every AI workload or every NVIDIA platform.
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Who owns which responsibilities?
The following division comes from NVIDIA’s self-hosted Kubernetes pattern. It is a useful starting point for assigning owners, not a universal allocation for every deployment.
| Party | Primary responsibility in this pattern |
|---|---|
| Model provider | Protect model weights and serving code, and set the release policy. The provider may operate or delegate the verifier, reference-values service and key-release service that gate model access. |
| Enterprise data owner | Decide which inputs are approved, where outputs may go, and which operational data may be logged or retained. |
| Platform operator | Run Kubernetes and the underlying hardware; manage firmware and GPU mode; secure networking and storage; monitor the environment; handle incident response and approved data paths. The VM reference likewise leaves availability and operations with the operator. |
| Confidential-computing software provider | Supply the runtime, attestation and measurement components, GPU integration, key-release layer, support matrix and failure signals. |
| Security team, OEM, integrator and application team | Review trust boundaries, validate the integrated stack and connect the service to approved workflows. |
NVIDIA summarizes the rationale in its Confidential Containers Reference Architecture: “A zero-trust posture on cloud-native platforms such as Kubernetes is essential to secure assets (model IP and enterprise private data) from untrusted infrastructure with privileged user access.” That is the document’s architectural principle, not a claim that adopting the reference eliminates operator risk.
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What must operators verify and secure themselves?
Use this checklist to turn the architecture into deployment controls. Assign an owner and retain evidence for each item.
- Define the boundary. Record whether the workload uses confidential containers on Kubernetes, a confidential VM, DGX BasePOD infrastructure or another pattern. Document what is in scope, especially whether the workload is inference, training or another activity.
- Validate the target profile. Confirm that the actual hardware, firmware, GPU confidential-computing mode and software versions are covered by the applicable support and validation profile. NVIDIA’s VM reference says components must be confirmed against the target validation profile.
- Test the attestation and release path. Check that the measured runtime and fresh evidence cover the intended CPU, GPU, guest, image, runtime and firmware state. Verify that policy mismatches, missing evidence and invalid collateral fail closed, and that no alternate path releases model secrets.
- Protect model material. Keep model assets encrypted outside the confidential guest and release decryption secrets only after successful policy checks. In the Kubernetes pattern, do not store those secrets in Kubernetes Secrets or host-visible paths.
- Secure the operator-owned layer. Set controls and named ownership for cluster or VM administration, network and storage access, monitoring, incident response and availability. Define how those controls are audited.
- Set data and telemetry rules. Specify who may record prompts, responses and operational signals, where records may be stored, and how long they may be retained. Audit security events without logging model keys, prompts, responses, weights or customer data.
- Review application protections separately. Where needed, implement and validate model-server authorization, guardrails and application-level tenant separation; the cited VM reference places these features outside its scope.
What the architecture does not guarantee
Attestation can help a relying party decide whether a measured environment meets a policy; it cannot prove that the policy is appropriate, that all relevant evidence has been included, or that the application behaves safely. Likewise, a reference architecture describes components and integration points, not the security state of a deployment. Operators and data owners still need to decide what they trust, validate the implementation, and govern the data and service around the protected workload.
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NVIDIA’s documentation is versioned and support can vary by hardware and software profile. Check the current compatibility and validation information for the specific system being deployed rather than assuming that a capability in one reference applies to every NVIDIA platform or workload.
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