Enterprise Zero-Trust Data Governance for Private On-Premise LLM Deployments

Running LLMs on-premise solves data residency, but not data governance. A private model with access to every document can still leak salary data to an intern. Zero-trust principles — never trust, always verify, least privilege — must extend into the model layer.

Identity flows end to end

Propagate the user's identity token from the client through the orchestration layer to the retrieval service. Retrieval filters by the user's group memberships at query time; the model never sees documents the user cannot open directly.

User ─(OIDC token)─► Gateway ─► Orchestrator ─► Retriever(ACL filter)
                                   │
                                   └─► LLM (isolated namespace, no egress)

Network isolation

• Serve models in a dedicated namespace with default-deny NetworkPolicies.
• Block internet egress from inference pods.
• Use mTLS between services via a service mesh.
• Sign and verify model artifacts and container images.

Prompt-injection defense

Ingested documents can contain instructions targeting the model. Scan content at ingestion, mark retrieved text as untrusted data in prompts, and never grant agents tools whose misuse would be catastrophic without human confirmation.

Output controls

Apply PII and secret detection on outputs, redact before display, and log redaction events for security review.

Auditability

Log the user, prompt template version, retrieved document IDs, model version and output hash for every request. Store logs immutably with retention aligned to your regulatory obligations. These records make AI systems inspectable by security teams and auditors in the same way as any other critical system.