Professionals who handle confidential information, such as lawyers, accountants, clinicians, HR staff and engineers working under NDA, often want AI help but cannot paste client material into an online service. Local language models offer a path: the model runs on a machine you control, and prompts never leave it. But "local" is only the starting point. A careful setup still needs attention to several details.
Verify that it is really local
- Confirm the application works with the network disconnected after models are downloaded.
- Check whether the tool sends telemetry, crash reports or usage statistics, and disable them if possible.
- Prefer open-source runtimes whose behavior can be inspected.
Control where data is stored
Chat histories, uploaded documents and vector indexes are stored on disk. Know the folder locations, protect them with full-disk encryption, and include them in your retention policy. Delete what you no longer need.
Restrict network exposure
Local model servers usually listen on a port. Keep them bound to localhost on a personal machine. If colleagues need access, place the service behind authentication on the internal network only, never directly on the internet.
Apply access control to documents
When a shared assistant can search internal documents, it must respect permissions. Filter retrieval results by the user's rights before passing passages to the model. A model cannot keep a secret it has been given.
Keep humans in the loop
Local models can still make mistakes or invent details. For legal, medical or financial work, treat outputs as drafts and verify against source material. Ask the model to cite which passages it used.
Choose models responsibly
Use models from known publishers with licenses that permit your use. Verify downloads and keep a record of which model version produced which outputs if your field requires audit trails.
Maintain the system
Update the operating system, drivers and AI software regularly. Old versions may contain security issues. Assign a person to own maintenance.
Plan the hardware realistically
Privacy does not require the largest model. Many drafting and summarizing tasks work well with mid-sized models on a single GPU or a Mac with sufficient memory. Size hardware to the tasks, not to marketing.
When to get help
If several people need a private assistant, a turnkey build with configured access control and logging can be faster and safer than assembling everything yourself.
References
- MicroAI One custom build and enterprise delivery: https://microaione.com/solutions/
- MicroAI One shared local LLM for small teams: https://microaione.com/solutions/small-team-shared-llm/
Published by MicroAI One (本地AI 1号网), a Shenzhen team working on local LLM hardware, software and deployment.