Managing Company Data with Local LLMs: Permissions, Logs and Updates

Operating a local LLM requires control of retrieval permissions, logs, external communication and updates as well as document storage. Hosting a model on an internal server does not, by itself, prevent leakage or incorrect answers.

Contents

1. Assigning responsibility for local operation
2. Local deployment changes the processing location and control boundary
3. Map communication, permissions, storage and operations
4. Evaluate open-weight models on the intended work
5. Measure useful work and the effort needed to check it
6. Plan how work continues during an outage
7. Discuss your project

Assigning responsibility for local operation

Item	What to check	How to assess it
Permissions	Transfers, departures and document scope	Align source and retrieval permissions
Updates	Model, software and document versions	Repeat the evaluation after changes
Incidents	Shutdown, restoration and notification	Do not fall back to an unauthorised provider

Local deployment changes the processing location and control boundary

A local LLM can support search and summarisation of documents that cannot be sent to external AI services. Keeping inference, retrieval and logs within a controlled environment reduces those external data flows. It does not eliminate compromised endpoints, privileged misuse, excessive permissions or disclosure through answers.

Map communication, permissions, storage and operations

Draw the path from user authentication to retrieval, inference and storage. Include any external OCR, transcription, updates and diagnostic uploads. Document permissions must also apply during retrieval; a login page in front of an unrestricted combined index is insufficient.

Test transfers, departures, document deletion and permission changes. A document removed from the source should not remain accessible through an old index or cache.

Evaluate open-weight models on the intended work

Benchmark scores from different methods or model versions are not interchangeable. Test the Japanese documents, tables, exceptions and source citations that users actually need. Check licences separately from technical availability.

Fine-tuning does not guarantee higher accuracy. Retrieval improvements may be more suitable when documents change frequently and answers need evidence. Keep evaluation examples separate from training data and check for regressions after updates.

Measure useful work and the effort needed to check it

Local processing may make new workflows permissible, but permission to use AI is not proof of a financial return. Count searching, checking, correcting and maintaining the system. An industry-wide average breach cost cannot simply be booked as savings from a local deployment.

Plan how work continues during an outage

Test restores, access revocation and model rollback as well as answer quality. Keep a fallback way to find original documents. LinkT&B helps define the information, users, evaluation criteria and operating responsibilities before selecting the deployment. NIST Generative AI Profile.

Discuss your project

We help select an AI architecture, evaluation process and operating model for your workflows and information requirements.

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