The business side of "Local FAQ refuse policy"
Refuse medical, legal, and payroll advice; log refusals. Private deployment of large language models is attractive for companies that cannot send documents to public APIs. The technology is accessible; the harder part is governance: who may use the system, for what data, and who is accountable when it is wrong.
Start with data classes
List the kinds of data the system will see: public material, internal documents, customer records, financial data. Decide which classes are allowed, where they are stored, and how long logs are kept. Write this on one page and get it signed by whoever owns compliance. Local inference does not remove the need for a policy; it makes the policy enforceable.
Access and keys
- Give each person or service its own key; avoid a shared team key pasted in chat.
- Rotate keys on a calendar and revoke them on offboarding the same day.
- Keep SSH access to inference hosts key-only, with a documented break-glass procedure.
- Log the caller, model version and timestamp for every request.
Guardrails and testing
Put simple input and output filters in front of the main model, version their rules, and test them. Run a light red-team exercise before each launch: prompt injection through documents, attempts to extract other users' data, and requests the assistant should refuse. Tools that can write or send things should start disabled and be added only after a read-only phase.
Cost and delivery
Explain costs to finance with measured numbers: hardware, power from a plug meter, admin time and evaluation effort, compared with a realistic cloud scenario. A delivery package for a private deployment should include the hardware bill of materials, a runbook, evaluation results and an access matrix.
A worked example
A professional services firm set up a local assistant for drafting proposals. Before launch they classified data into three levels and allowed only the first two. Each consultant received a personal key, logs recorded the caller and model version, and an output filter blocked client names from appearing in drafts meant for other clients. A short internal test found that pasted documents could carry instructions that the model followed, so they separated retrieved text from instructions and added a review step. The launch was slower than a demo, but the security team signed off and the service has an owner who answers questions.
Further reading from MicroAI One: custom local-AI builds, and private deployment services.
Bottom line
Local AI rewards patience and record keeping more than raw spending. Decide what the system must do, measure it on your own hardware and documents, write down every change with a date and an owner, and review the setup after each significant upgrade. Small, documented steps are easier to debug, easier to hand over and easier to defend when someone asks why a particular choice was made. When in doubt, test on a smaller scale first and expand only when the evidence supports it.
Quick reminders
- Test a restore, not just a backup.
- Log the model name and version with every request.
- Keep the previous model file until the new one passes your eval set.
- Cap concurrent sessions before lowering quantisation.
Published by MicroAI One (本地AI 1号网), a Shenzhen team working on local LLM hardware, software and deployment.