AI Governance for On-Premise LLMs
Versioning, logging, access control and regulation — the operating discipline that makes local AI defensible.
Self-hosting removes the vendor from your trust chain — and hands you everything the vendor used to do. Governance is that inheritance, made explicit: who may use which model on which data, what gets logged, what changes when, and who answers for it. Done well, it is also the on-premise superpower: every obligation regulators care about becomes evidence you physically control.
The Six Pillars
01 · Model governance
- Provenance: download weights from the official source, verify checksums, archive the model card and license. A model file is a supply-chain artifact like any dependency.
- Versioning & freeze: production systems pin an exact model version; upgrades go through evaluation, not enthusiasm. This is the advantage no API gives you — nobody can change the model under your validated system.
- License register: "open-weight" ≠ do-anything. Record each model's license and permitted uses; re-check when you productize (see the provider-vs-deployer trap).
02 · Data governance
- Classification first: which data classes may reach which deployment level (local / rented GPU / API)? Enforce in the gateway, not in a policy PDF.
- RAG permissions: retrieval must filter by the requesting user's rights (ACL-aware RAG). Indexing the shared drive without this is the #1 enterprise leak pattern.
- Training-data records: if you fine-tune, keep the dataset, its provenance and its consent basis on file — both the AI Act and GDPR will ask.
03 · Access control
- SSO in front of every chat UI and API endpoint; per-team model/document scopes; no shared service accounts.
- Separate user access (asking questions) from operator access (changing models, prompts, indexes) — the second is a privileged change path with review.
- Egress rules: an inference box needs no outbound internet at runtime. Enforce it; that's the sovereignty you paid for.
04 · Logging & audit
- Log prompts, retrieved sources, outputs, model version and user — this is both your quality dataset and your audit trail (mandatory for high-risk AI Act systems).
- Logs of an internal assistant contain PII and secrets by nature: encrypt, restrict, set retention, and tell employees (GDPR applies internally too).
- Make the trail reconstructive: from any answer, you can replay which model + which sources produced it.
05 · Evaluation & human oversight
- Keep a fixed eval set (~50–100 known-answer questions); run it before every model/prompt/index change. No eval = governance theater.
- Define where a human must stay in the loop — and give them real override power that gets used, not a rubber stamp (the AI Act's test for high-risk systems).
- Ban binding decisions (HR, legal, pricing, medical) in policy, visibly, in the UI.
06 · Regulatory mapping
- Inventory every AI use case; classify per EU AI Act risk tier (per function, not per project); track the phase-in dates. Full treatment: EU AI Act & on-premise guide and the observatory AI Act section.
- Align AI Act evidence with GDPR work you already do — DPIA and FRIA can share a base.
- Sector overlays (MDR, DORA, NIS2, GxP) ride on top: map them per workload, and reuse the same logs and version records as evidence.
For a small team starting out, the honest floor is one page: (1) a model register (version, checksum, license), (2) an allowlist of document sets in RAG with an owner each, (3) SSO + two access tiers, (4) query logging with retention, (5) a 50-question eval run on every change, (6) a one-line use-case register with AI Act tiers. Grow it when the deployment grows.
Disclaimer
The information provided on LLMOnPremise.com (a subsection of AI-RADAR) is for educational, research, and technical exploration purposes only. Nothing on this website constitutes professional legal, financial, or IT security advice.
- Security: Implementing air-gapped or local LLM solutions does not automatically guarantee data security. You are responsible for your own penetration testing and compliance (GDPR, HIPAA, EU AI Act, etc.).
- Hardware: Hardware recommendations (GPU sizing, VRAM requirements) are based on theoretical models and community benchmarks. Real-world performance varies.
- Reliability: Open-weights models (Llama, Mistral, etc.) are experimental software. We do not guarantee their fitness for mission-critical production systems.
- Enterprise liability: You assume full responsibility for deploying these technologies in your infrastructure. AI-RADAR and its contributors disclaim all liability for data loss, security breaches, or hardware damage resulting from following guides on this site.
System Status: [INFO_ONLY] - Professional validation required for production.