On-Prem AI Control Plane Prerequisites: Network, Identity, Storage, and Key Management Checklist

Why prerequisites matter more for enforcement in the execution path

A runtime enforcement layer sits between callers and the things they call: models, agents, tools and MCP servers. For each interaction or tool call it can allow, redact, block, rate limit or escalate for human approval. That position is what makes it useful, and it is also why environmental gaps show up as production problems rather than documentation problems.

If a network path is missing, enforcement is bypassed or requests fail. If identity is incomplete, a decision is recorded but cannot be tied to a person, service or agent. If storage is undersized or retention is wrong, evidence disappears before an auditor asks for it. If keys are poorly managed, the evidence exists but cannot be trusted or is exposed. NIST's guidance on AI security overlays notes that AI system security is closely intertwined with the security of the underlying infrastructure, and that overlays assume baseline controls already exist. Treat the four domains below as that baseline.

Network: make enforcement non-bypassable and measurable

The central network question is whether enforcement points are always invoked. NIST zero trust material describes enforcement infrastructure as non-bypassable, verifiable and independent of application code. In practice, map every path from agents, applications and coding assistants to model endpoints, tool servers and MCP servers, then test that no route avoids the enforcement point. Do this with deliberate attempts to go around it, not by reading diagrams.

Segmentation helps, but network location alone is not trust. NIST states that network-tier policy must be augmented with policies that establish trust in the identity of users and services, whether they run on-premises or in multiple clouds. Pair segmentation and egress allow-lists with authenticated service identity. Confirm that TLS or mutual TLS is available between callers, the enforcement layer and downstream endpoints, and that DNS resolution works for internal model, tool and MCP endpoints from the enforcement zone.

Then define behavior under stress. Agree a latency budget for the added enforcement hop at the interaction level, and measure against it with representative payloads. Decide whether the layer blocks or passes traffic through when it cannot reach a decision, and record who approved that choice. OWASP recommends per-session or per-tenant rate limits, quotas and timeouts for MCP, and these interact with enforcement latency and failure handling, so test them together under load with enforcement active.

Identity: attribute every decision to someone or something

Enforcement decisions are only useful as evidence if they identify who or what acted. Start by confirming that baseline identification, authentication, account management and configuration management controls already exist, since NIST's AI overlay work assumes them. Then cover four populations: human administrators, services and workloads, agents, and approvers.

Administrators should authenticate through your SSO provider using OIDC or SAML, with RBAC that separates people who write policy from people who approve or deploy it. NIST guidance on agent identity recommends least privilege and separation of duties so an agent cannot assume conflicting privileges that bypass governance controls. Apply the same logic to policy administrators.

For services and agents, issue workload identities and use short-lived, scoped credentials for MCP servers and tools, with secret scanning to catch leakage. OWASP identifies failures to verify identity or enforce access during MCP interactions as a leading risk. Agent identity and naming models are still maturing, so write down which assumptions are provisional and review them periodically.

Finally, human escalation needs an identifiable authority. Define the risk thresholds that trigger review, name the owner of each approval route, and confirm that approvers authenticate strongly and that their identity is recorded with the decision.

Storage: size for volume, set retention deliberately

Evidence generated by enforcement accumulates quickly because every decision on an interaction or tool call can produce a record. As a scale reference, Trussed customers generate 30+ GB of audit evidence per day in production, so plan capacity from your own traffic profile and growth rather than from a default.

Settle retention with compliance owners before installation. Evidence from enforcement can be mapped to frameworks such as HIPAA, NAIC, NYDFS, the EU AI Act (Regulation (EU) 2024/1689) and NIST, but the retention period and the evidence you must be able to produce depend on which obligations apply to your organization and use cases. Have counsel confirm applicability against current official text. Confirm that the storage layer can enforce the chosen period and handle deletion at its end.

Then check the properties that make evidence durable. Decide whether you need write-once or other tamper-evident storage so records cannot be altered quietly. Confirm data residency, so evidence stays in the required jurisdiction, including replicas and backups. Define backup and recovery objectives for the evidence store, and test a restore. Trussed supports self-managed deployment in the customer cloud with configurable retention and data residency, so these choices remain yours.

Key management: protect both evidence and policy configuration

Two assets need protection: the audit evidence and the policy configuration that determines what is allowed. NIST's overlay concept paper lists configuration settings among AI system components, which supports treating policy as a protected asset and not only the data it produces.

Confirm that your KMS or HSM can serve the enforcement layer and evidence stores, that encryption at rest and in transit is on by default, and that key rotation has an owner, a schedule and a tested procedure. Check that rotation does not break reading older evidence within the retention period.

Separate duties across key administrators, policy administrators and evidence readers, so no single role can change policy, alter evidence and hide the change. Restrict access to evidence stores by role, and record that access. Keep credentials for tools and MCP servers short-lived and kept out of configuration files, consistent with the secret-scanning practice above.