Data & privacy
Keeping business data private with local AI
Local inference can keep prompts and model responses inside your own environment. To keep the whole workflow private, you also need to account for document processing, connected tools, stored chats and backups.
Follow a document from upload to answer
Imagine an employee asking a question about an internal product document. A chat application receives the upload, a parser extracts its text, a search index may store it, and the model receives relevant passages alongside the question. The application may then save the conversation.
Each of those stages has a location and an owner. Keeping the model on your premises is one part of the design. The parser, search index and conversation store also need to be considered. Ask where each component runs, what it retains and which accounts can reach it.
Check the selected model and connected services
A web interface is not evidence that inference is happening in the cloud, and a server in your office is not evidence that every request stays there. The selected model endpoint determines where the model receives its input.
Open WebUI supports local and hosted connections. Its documentation also distinguishes local model inference from separately configured cloud tools and document services. For sensitive material, make the approved local connection easy to identify and restrict other connections according to your policy.
Private from a provider does not mean private from every colleague
Your own access controls determine who can read documents, use a model and administer the service. A shared index containing payroll documents should not become searchable by everyone simply because the server is local.
Use individual accounts where appropriate, separate administrative access from everyday use, and decide how documents are grouped and shared. Treat retrieved text and generated answers as potentially sensitive. Check that document permissions are respected when answers are assembled, rather than only when files are uploaded.
Make retention and recovery explicit
Decide how long uploaded files, chat history and logs need to stay available. Document how an employee leaves the service, how a file is removed and what happens to copies in backups. Encryption and access restrictions should cover the storage as well as the network connection.
Backups need their own protection. A copy sent to an external backup provider changes where the data is held, even if inference remains local. Restore testing, update ownership and an agreed support process help turn a privacy intention into an operating practice.
Questions to settle before installation
Write down the documents and systems the service may access, the people who may use them, and the external connections that are allowed. Include remote support in that discussion: agree how support access is granted and removed, and what information support staff can see.
Local AI gives you control over these decisions. It does not certify a business as compliant or make generated answers correct. The useful outcome is a clear, testable data path that your team understands and can maintain as the system grows.
Sources & further reading
Official documentation behind the technical details in this guide.