How should teams secure AI workloads running on premises?

Why This Matters for Security Teams

On-premises AI workloads often sit inside a trusted network while still behaving like internet-facing software. That is the risk: the model itself is not the only asset. The request path usually includes users, gateways, retrieval layers, secrets, plugins, and tool calls, each of which can expand the blast radius if it is not governed. Current guidance suggests treating the workload as a machine identity problem as much as an AI problem, which is why Ultimate Guide to NHIs — What are Non-Human Identities remains relevant even for private deployments.

Security teams also underestimate how quickly secrets and machine identities sprawl across internal platforms. NHIMG research on The State of Secrets in AppSec notes that 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, while machine identity reporting shows many organisations still lack complete inventory and automated lifecycle control. Those conditions make on-prem AI attractive to attackers because lateral movement can stay entirely inside the estate. In practice, many security teams encounter credential exposure only after an internal agent, connector, or retrieval job has already accessed data it should never have seen.

How It Works in Practice

Securing on-prem AI starts by governing the full execution path, not by assuming the network boundary is enough. The first control point is authentication and request provenance: know who initiated the task, which system submitted it, and what policy approved the context bundle. The second is workload identity for the AI service itself. For that, the SPIFFE workload identity specification is a practical foundation because it gives cryptographic proof of what the workload is, not just where it runs.

From there, teams should separate the model from its tools. Retrieval sources, vector stores, file shares, internal APIs, and code execution hooks need independent authorization and logging. Best practice is evolving toward runtime policy evaluation rather than static allow lists, using policy-as-code to decide whether a particular prompt, context item, or tool invocation is allowed at that moment. For agent-like systems, this matters because the sequence of actions is not fully predictable in advance.

• Issue short-lived credentials per task instead of long-lived static secrets.
• Bind context injection to a policy that limits data scope by user, task, and sensitivity.
• Log prompt, retrieval, and tool-call decisions as a single audit trail.
• Revoke access automatically when the job completes or the policy changes.

NHIMG’s Guide to SPIFFE and SPIRE is useful here because it maps workload identity to practical issuance and rotation patterns, which is essential when secrets cannot be left sitting in long-lived config files. These controls tend to break down in legacy on-prem environments where shared service accounts, manually mounted certificates, and flat network segments make it impossible to distinguish one AI workload from another.

Common Variations and Edge Cases

Tighter control over on-prem AI often increases operational overhead, so organisations must balance containment against deployment speed. There is no universal standard for this yet, especially for agentic systems that chain tools or hand off between services. In some environments, a classic gateway plus DLP layer is enough for passive inference workloads. In others, especially where code execution or internal actioning is enabled, that model is too weak because the workload can make new decisions after the initial request is approved.

One common edge case is shared infrastructure for development and production. That setup makes identity boundaries blurry and turns test prompts into a source of policy leakage. Another is air-gapped or highly regulated environments, where teams may assume lower exposure and therefore delay certificate automation and secret rotation. NHIMG research on machine identity complexity shows why that assumption is risky: manual tracking and delayed remediation become worse as the estate grows. When this happens, Ultimate Guide to NHIs — Standards is most helpful as a governance reference, while current guidance suggests aligning on shorter TTLs, per-service identities, and explicit approval for every tool that can change state.

For teams evaluating governance maturity, the practical question is not whether the model is local, but whether each request can be explained, constrained, and revoked before the next one starts. That is where on-prem AI security succeeds or fails.

Related resources from NHI Mgmt Group

• How should teams secure non-human identities across cloud and SaaS?
• How should teams combine SAST and DAST in a secure development programme?
• How should security teams reduce risk from weak SSH access on Linux workloads?
• How should security teams decide whether JIT access is safe for non-human identities?