Private inference and secure agent execution solve two different problems.
NVIDIA NIM helps enterprises deploy model inference inside cloud, data-center, Kubernetes, VM, and other supported environments.
NVIDIA OpenShell focuses on the agent runtime: restricting what autonomous software can access while it executes.
Put them together and you get a useful architecture:
Secure agent execution
+
Private model inference
That is a strong foundation for enterprises that want autonomous systems without automatically sending sensitive context to public model endpoints.
Separate the concerns
An enterprise agent has at least two major planes.
Execution plane
Where the agent reads files, runs commands, invokes tools, accesses APIs, and uses credentials.
Inference plane
Where the model receives prompts/context and generates output.
Those planes can be secured independently.
Agent Sandbox --------> Private Model
| |
execution policy inference policy
OpenShell secures the left side. NIM can provide the right side.
What NVIDIA NIM provides
NVIDIA NIM microservices package model-serving runtimes into deployable containers with standard APIs.
For LLM deployments, NIM supports production-oriented inference across NVIDIA GPU infrastructure and can be deployed in Kubernetes, virtual machines, cloud environments, and data centers.
NVIDIA also documents air-gapped deployment patterns where model assets are staged locally and the inference environment operates without internet access.
That makes NIM relevant for organizations with strong data-residency or network-isolation requirements.
What OpenShell adds
Private inference alone does not secure the agent.
Imagine:
Agent -> private NIM
But the same agent still has unrestricted filesystem access, raw cloud credentials, open internet, production access, and every MCP tool.
The model is private. The agent is still overprivileged.
OpenShell addresses this second layer through sandboxed execution, filesystem policy, process restrictions, network policy, provider credential mediation, workspace boundaries, and centralized gateway management.
Reference architecture
Enterprise Users
|
v
Enterprise Identity
|
v
OpenShell Gateway
|
v
Agent Sandbox
/ | \
/ | \
Filesystem Network Credentials
|
v
Private NIM API
|
v
NVIDIA GPUs
Surround that with Kubernetes, Vault or cloud secrets, OIDC, SIEM, GitOps, and network segmentation.
Keep inference inside the approved boundary
One security objective may be:
Source-code context must not leave our controlled environment.
The network policy can therefore allow:
inference.company.internal
while denying unapproved model endpoints.
The agent is technically prevented from choosing another model destination.
That is stronger than relying on configuration conventions.
Separate provider policy by workload
Not every workload has the same data sensitivity.
Public Research Agent
-> approved external model
Engineering Agent
-> internal NIM
Restricted Data Agent
-> isolated NIM environment
This lets security requirements drive inference architecture.
Deploy NIM on Kubernetes
NVIDIA supports Kubernetes deployment patterns for NIM, including NIM Operator-based workflows.
That creates a natural architecture for enterprises already operating GPU-enabled Kubernetes:
Kubernetes Cluster
|
+-- OpenShell Gateway
+-- Agent Sandbox A
+-- Agent Sandbox B
`-- NIM Service
|
`-- GPU
Whether agents and NIM belong in the same cluster, separate clusters, or separate network zones depends on the organization's threat model.
Use network segmentation intentionally
Private inference should not simply mean "the endpoint has a private DNS name."
Define the path:
Agent namespace
|
NetworkPolicy
|
NIM service
|
GPU nodes
Then decide which sandboxes may connect, which ports are open, what identity is used, whether mTLS is needed, whether other namespaces can reach inference, and whether model-management endpoints are exposed.
Credentials should remain outside the agent where possible
If NIM or supporting infrastructure requires authentication, avoid unnecessarily placing reusable credentials inside the autonomous process.
Use workload identity, short-lived tokens, secret mediation, or gateway-side credentials where appropriate.
Air-gapped patterns
Some regulated environments require minimal or zero external connectivity.
NVIDIA documents air-gap deployment for NIM where required model artifacts are downloaded during a connected preparation phase and then transferred into the isolated environment.
Pair that with an OpenShell runtime whose network policy is default deny, and the resulting architecture can support tightly constrained execution.
Air-Gapped Environment
|
+-- OpenShell
| `-- Agent Sandbox
+-- NIM
+-- Internal MCP/API Services
`-- Internal observability
Do not confuse private with secure
Private infrastructure can still be poorly secured.
A private agent environment should also address identity, least privilege, east-west network policy, credential isolation, supply-chain controls, container security, logging, change management, and vulnerability management.
Private inference is one control. It is not the entire security program.
Add security observability
The security team should be able to observe both planes.
Agent runtime
- denied network requests;
- sandbox lifecycle;
- policy changes;
- provider use.
NIM
- service availability;
- inference request metrics;
- latency;
- GPU utilization;
- errors.
Enterprise security
- workload identity;
- network telemetry;
- Kubernetes audit events;
- SIEM correlation.
Policy-as-code becomes the operating model
A strong platform defines:
agent template
+
OpenShell policy
+
provider configuration
+
NIM endpoint
+
identity
+
network controls
as code.
Teams request an approved environment rather than assembling one ad hoc.
The bigger idea: private autonomy
The enterprise value proposition is not just private inference.
It is private autonomy:
models under control
+
agents under control
+
credentials under control
+
data movement under control
+
tools under control
That is a more complete vision of enterprise AI infrastructure.
How Anpu Labs helps
Anpu Labs designs and implements secure private-agent platforms using OpenShell, NIM, Kubernetes, identity, secrets management, and observability.
Our assessment determines which workloads need private inference, which agent capabilities are required, which systems and data are involved, how OpenShell policy should be structured, how NIM should be deployed, and how the environment should integrate with enterprise security.
References
- NVIDIA NIM: https://docs.nvidia.com/nim/
- NVIDIA NIM LLM/VLM Installation: https://docs.nvidia.com/nim/large-language-models/latest/get-started/installation.html
- NVIDIA NIM Operator Deployment: https://docs.nvidia.com/nim/large-language-models/latest/deployment/kubernetes-deployment/nim-operator-deployment.html
- NVIDIA NIM Air-Gap Deployment: https://docs.nvidia.com/nim/large-language-models/latest/deploy-air-gap.html
- NVIDIA OpenShell — How OpenShell Works: https://docs.nvidia.com/openshell/about/how-it-works
