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Short answer: NVIDIA’s AI infrastructure security designs can help protect sensitive data and model assets while they are being processed, using hardware-backed confidential computing, isolation, attestation and policy-controlled key release. They do not secure an entire AI service automatically. The customer operating the platform still has to configure and protect its infrastructure, set data and access policies, monitor the service and respond to incidents. The exact boundary depends on the deployment pattern.
What the security boundary actually covers
Confidential computing aims to protect data during use: while it is being processed, rather than only when it is stored or transmitted. NVIDIA’s confidential-computing materials describe CPU and GPU capabilities that can help isolate workloads and verify aspects of the environment in which they run. In the documented designs, attestation supplies evidence about that environment; policy can then determine whether a service should release the keys needed to decrypt a workload or its model assets.
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That is a defined execution boundary, not an end-to-end security guarantee. Confidential computing does not by itself establish that an application authorizes users correctly, that its outputs are safe, that tenants are properly separated, or that the surrounding network, storage and operations are secure. Those controls must be addressed in the deployed system.
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How attestation and key release work together
Attestation is a way for a verifier to evaluate evidence about the machine and workload that are requesting access to a protected resource. In NVIDIA’s self-hosted Kubernetes reference pattern, that evidence is intended to cover the CPU, GPU, guest environment, workload image, runtime policy and firmware state. A policy can allow key release only when the evidence meets the required conditions.
- Measure the environment. The deployment records measurements and other evidence for the relevant hardware, guest and workload components.
- Evaluate the evidence. An attestation verifier checks the evidence against policy and expected reference values.
- Release secrets only on approval. A key-release service makes the required secret available only after successful checks, allowing an approved workload to access encrypted assets.
- Reject failures. Missing, stale or mismatched evidence, policy or supporting collateral should prevent key release rather than trigger a permissive fallback.
Attestation is only as useful as the evidence and policy actually checked. Operators need to confirm that the checks cover the components and states that matter to their workload, that evidence is sufficiently fresh, and that a failed check really blocks access. NVIDIA’s Self-Hosted Kubernetes Confidential Computing Reference Architecture also calls for model assets to remain encrypted outside the confidential guest and for secrets not to be placed in Kubernetes Secrets or host-visible paths.
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Which deployment pattern are you using?
The documented patterns have different execution boundaries and workload scopes. A Kubernetes confidential container is not interchangeable with a confidential VM, and neither should be treated as a description of every NVIDIA AI system.
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| Pattern | Documented boundary and scope | What to verify |
|---|---|---|
| Confidential containers on self-hosted Kubernetes | The NVIDIA Confidential Containers architecture describes Kata-based sandbox isolation, GPU passthrough, composite attestation and key release for encrypted workloads. | Check the confidential runtime class, measured sandbox, confidential-computing GPU, attestation verifier, key-release service, evidence and policy coverage, and audit-log limits. |
| Self-hosted confidential VM | NVIDIA’s self-hosted VM architecture covers GPU-accelerated inference inside a confidential VM, including CPU and GPU confidential computing, remote attestation, policy-controlled key release, model-image lifecycle, network controls and operational signals. | Confirm the target hardware and software validation profile and define who owns availability and operations. This document excludes Kubernetes-native confidential containers, training and fine-tuning, fleet orchestration, and model-server authorization, guardrails and application-level multi-tenancy. |
| DGX BasePOD infrastructure | The DGX BasePOD reference architecture describes an enterprise infrastructure context: DGX compute, InfiniBand compute fabric, Ethernet management and storage networks, out-of-band management networks, management servers, storage partners and NVIDIA software. | Treat it as an infrastructure architecture, not as proof that the workload’s confidential-computing boundary or customer security duties have been satisfied. |
The DGX BasePOD document is identified as RA-11127-001 V5, published August 6, 2025. Its infrastructure scope does not make the confidential-container and confidential-VM patterns equivalent or establish security for every workload deployed on BasePOD.
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Who is responsible for what?
The following division of duties comes from NVIDIA’s self-hosted Kubernetes reference pattern. It is useful for assigning owners, but it is not a universal responsibility model for every NVIDIA deployment.
| Role | Responsibilities in the Kubernetes pattern |
|---|---|
| Model provider | Protect model weights, serving code and release policy. 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 manage hardware, firmware, GPU mode, networking, storage, monitoring, incident response and approved data paths. |
| Confidential-computing software provider | Supply the runtime, attestation, measurement, GPU integration, key-release layer, support matrix and failure signals. |
| Security team, OEM, integrator or application team | Review trust boundaries, validate the stack and connect the service to the organization’s workflows. |
These roles can sit within one organization or be divided across vendors and customers. The important operational step is to assign each control to a named owner, including who approves policy changes and who investigates failed attestation or unexpected data flows.
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What the customer must still secure
Platform and infrastructure
- Protect the Kubernetes control plane or VM-management layer, administrator access and the systems used to deploy workloads.
- Manage hardware and firmware state, GPU confidential-computing mode, network segmentation, storage access and availability.
- Monitor the environment, maintain incident-response procedures and define who can change configuration or access operational systems.
Application behavior and data governance
- Enforce application-level authorization, model-server guardrails and tenant isolation where the service requires them; the cited VM architecture does not claim to supply these features.
- Approve data inputs and output destinations, and determine which prompts, responses and telemetry may be retained, by whom and where.
- Keep security audit records appropriately limited. NVIDIA’s Kubernetes reference advises against logging model keys, prompts, responses, weights or customer data as audit content.
Validation and failure handling
- Check that hardware, firmware, GPU mode and software versions match the target validation profile and applicable support matrix.
- Verify that attestation evidence and policy cover the intended CPU, GPU, guest, image, runtime and firmware state.
- Test that missing or mismatched evidence blocks key release, and that the failure is visible to the people responsible for investigation.
A practical deployment checklist
- Name the architecture. Record whether the workload uses confidential containers on Kubernetes, a confidential VM, DGX BasePOD infrastructure or another pattern, and document what the applicable reference architecture does and does not cover.
- Confirm the validated profile. Match the intended hardware, firmware, GPU mode and software to the target support and validation information before relying on the design.
- Trace the secret path. Identify who evaluates attestation, which policy governs key release, where encrypted model assets live, and how the workload receives secrets without exposing them through host-visible paths.
- Exercise rejection cases. Check that stale, missing or mismatched evidence prevents release and produces a signal operators can act on.
- Assign operational owners. Make responsibility explicit for control-plane or VM management, network, storage, monitoring, availability and incident response.
- Set data and logging rules. Specify approved inputs and outputs and limit telemetry and audit records to what is necessary without recording keys or sensitive workload content.
- Review application controls separately. Confirm authorization, guardrails and multi-tenant protections in the application or model-serving layer rather than assuming attestation provides them.
NVIDIA’s NVIDIA Confidential Containers Reference Architecture says that “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 premise: the execution environment can be checked and secrets gated, but security still depends on a carefully scoped design and the controls maintained around it.
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