# On-Prem AI Security Hardening Checklist for Self-Hosted LLMs > Source: https://netray.co/tools/on-prem-ai-security-checklist > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer A practical control checklist for securing self-hosted language models, covering model provenance, network isolation, data governance, host hardening, and audit readiness. ## Category on-prem-ai ## Tool **Kind:** checklist **Groups:** #### Model supply chain and provenance **Items:** **Text:** Every model weight file is downloaded from a verified source and its checksum recorded before deployment **Critical:** true **Text:** Models are stored in safetensors format rather than pickle-based formats that permit arbitrary code execution **Critical:** true An internal model registry records version, license, provenance, and approved use for each deployed model Third-party model licenses are reviewed for commercial and export restrictions before production use Fine-tuned and merged model artifacts are versioned in the registry alongside their base models and training data references A documented process exists for evaluating and retiring models when a superseding version is approved #### Network isolation and access control **Items:** **Text:** Inference endpoints sit on a segmented VLAN with explicit allow-lists, not on the general corporate network **Critical:** true **Text:** GPU hosts have no outbound internet access; model and package updates arrive through a controlled staging process **Critical:** true All API access requires authenticated service identities with per-application credentials, never a shared key Role-based authorization is enforced at the retrieval layer so users only see documents they are already entitled to Administrative access to GPU hosts requires multi-factor authentication and passes through a bastion or PAM system TLS terminates at the inference gateway with current certificates and modern cipher suites Rate limits and request size caps are enforced per identity to contain abuse and denial-of-service attempts #### Data governance and prompt handling **Items:** **Text:** Export-controlled and classified data flows are mapped and confirmed to remain inside the authorized boundary **Critical:** true Prompt and response logging policy is explicitly defined, with retention periods and a documented legal basis Sensitive fields are redacted or tokenized before text enters embeddings or long-term logs Vector databases and document stores are encrypted at rest with keys managed outside the AI platform Training and fine-tuning datasets are reviewed for confidential or personal data before any run begins A documented data deletion path removes a source document from the corpus, the vector index, and all derived caches #### Infrastructure and host hardening **Items:** GPU host operating systems are patched on a defined cadence with a documented maintenance window **Text:** Inference containers run as non-root with read-only root filesystems and dropped Linux capabilities **Critical:** true Container images are scanned for vulnerabilities before promotion and rebuilt on a fixed schedule GPU driver and CUDA toolkit versions are pinned, tested, and tracked in configuration management Secrets are delivered through a managed vault rather than environment variables baked into images Backups cover model artifacts, vector indexes, and configuration, with a tested restore procedure #### Monitoring, audit, and incident response **Items:** **Text:** All inference requests are logged with identity, timestamp, model version, and a request correlation ID **Critical:** true Logs are shipped to a tamper-evident SIEM outside the AI platform and retained per your compliance framework Anomaly alerts fire on unusual prompt volume, oversized inputs, and repeated jailbreak-style patterns Output filtering blocks known-sensitive patterns such as credentials, part numbers under export control, and personal identifiers An incident response runbook covers model compromise, prompt injection, and data exfiltration through generated output Red-team testing for prompt injection and data leakage is performed before launch and repeated at least annually **Scoring Note:** A self-hosted AI deployment is defensible when at least 90% of all items are complete and every critical item is closed. Any open critical item should block production launch for regulated workloads: in CMMC and ITAR environments a single unlogged inference path or an internet-reachable GPU host is enough to fail an assessment, and remediating after an incident costs far more than delaying a launch by two weeks. ## Content **Intro:** This free on-prem AI security hardening checklist covers the controls required to run self-hosted language models safely inside an enterprise network, and it is written for security architects, IT directors, and compliance leads at manufacturers with ITAR, CMMC, or customer flow-down obligations. It spans five domains: model supply chain and provenance, network isolation and access control, data governance and prompt handling, infrastructure hardening, and monitoring and incident response. Teams deploying private AI often carry over cloud-era assumptions that do not survive an audit, particularly around inference logging, retrieval-layer authorization, and model file provenance. **Sections:** #### Why on-prem AI needs its own control set **Body:** Bringing models inside your firewall removes the third-party data-sharing risk, but it introduces categories most security programs have never assessed. Model weight files are executable artifacts in some formats and can carry arbitrary code. Vector databases become a second copy of your document estate with a completely different permission model than the source systems. Prompt logs quietly accumulate the most sensitive questions your engineers ask, often in plaintext. And the retrieval layer will happily surface a restricted engineering drawing to any user who asks the right question unless authorization is enforced at query time rather than only at the document repository. #### The controls that matter most **Body:** Roughly a quarter of the items here are marked critical because they map directly to findings assessors raise, or to failure modes we have seen cause real incidents in production. The rest matter, but these are the ones that should block a launch rather than appear on a remediation plan. Each one closes a path that is difficult to detect after the fact: a compromised model artifact executes silently, an internet-reachable GPU host leaves no obvious trace until it is used, and missing inference logs mean an investigation has nothing to work with. Close these first, then work the remainder in priority order. **Bullets:** - Verified model provenance with recorded checksums and safetensors-only weight files, closing the code-execution path. - Segmented inference networks with no outbound internet access from GPU hosts. - Explicit mapping and confirmation that export-controlled data never crosses the authorized boundary. - Complete, tamper-evident inference logging with identity, model version, and correlation ID on every request. #### How to work through the list **Body:** Run it as a gated review rather than a one-time audit. Assign each domain an owner: platform engineering takes host hardening, security takes network and monitoring, and the data or compliance function takes governance. Score honestly, because a partially implemented control is an open control. Close every critical item before production traffic reaches the system, then work the remainder on a dated remediation plan. Re-run the full checklist whenever you change model versions, add a new document source to the corpus, or expand access to a new user population, since each of those events can silently invalidate an authorization or data-boundary assumption you validated months earlier. #### How Netray implements these controls **Body:** Netray deploys private AI for aerospace, defense, and electronics manufacturers where the security posture is the project, not an afterthought. We build inference platforms inside your boundary with segmented networking, permission-aware retrieval that mirrors your ERP and PLM entitlements, full audit logging into your existing SIEM, and documented evidence packages your assessors can actually use. Because we also build the SyteLine, LN, M3, and ServiceMax integration layer, entitlements stay consistent between the ERP and the AI assistant. Engagements typically start with a security architecture review that produces a control matrix mapped to your compliance framework. **Cta:** Get a control-by-control review of your private AI deployment mapped to CMMC, ITAR, and your customer flow-down requirements. **Faq:** #### Does running AI on-prem automatically satisfy ITAR and CMMC? **Answer:** No. On-prem deployment removes one large risk, which is technical data leaving US persons control through a third-party API, but every other control still applies. Assessors will ask about access control, audit logging, media protection, configuration management, and incident response for the AI system exactly as they would for any other system handling controlled unclassified information. On-prem makes compliance achievable; it does not make it automatic. #### What is the biggest security gap you see in private AI deployments? **Answer:** Authorization at the retrieval layer. Teams index every document they can reach, then rely on the chat interface to be discreet. It will not be. If a user can query the assistant, they can reach anything in the index, including restricted drawings, supplier pricing, and personnel records. The fix is filtering candidate documents by the requesting user's entitlements before they reach the model, enforced in the retrieval pipeline rather than in the prompt. #### How should we handle prompt and response logging? **Answer:** Log deliberately, with a written policy. You need identity, timestamp, model version, and a correlation ID on every request for audit and incident response. Whether you retain full prompt text is a separate decision that depends on your data classification and legal position. Many regulated customers retain metadata indefinitely, retain content for a short window for debugging, and redact sensitive patterns before anything is written. Never let full-text prompt logs accumulate without an owner and a retention period. ## Search terms on-prem AI security checklist, self-hosted LLM security, LLM deployment hardening, AI model supply chain security, air-gapped AI security controls, CMMC AI compliance checklist, GPU server hardening checklist --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json