How do you use a private LLM with enterprise data?

A workforce manager asks why capacity changed last month. The explanation may need department totals and definitions, while individual employee details add nothing to that question. Starting with the required information helps define a useful deployment.

Follow that information through retrieval, inference and delivery. Include the records that remain afterwards, because a saved conversation or log may retain part of the same request.

Define the data boundary first

A private LLM design starts with a decision about which information may be processed where. Name the locations for the application, model service and stored data. This establishes what private means for the proposed work.

Identify the content each connection carries. A schema can guide query planning. Query results can support an explanation. Document excerpts may require a separate retrieval path and access decision.

Choose that context from the task. A grouped capacity comparison can use totals, while an individual investigation may need records. Apply the organization's requirements to the information actually involved. The design review can then assess the processing route required for that task.

Locate the model and application

The application's location does not, by itself, tell you where inference takes place. Trace its model request to the service that processes it. An application in a private cloud can still use a separately managed endpoint.

On-premises inference brings operation of the model infrastructure into the organization. Capacity, recovery, monitoring and updates all need support. That can fit a local-processing requirement when the team can operate the service.

A managed endpoint assigns some responsibilities to the provider. Microsoft documents how deployment choices and features affect processing and retained state for models sold by Azure.

Assess those properties separately. Network access, storage location and restrictions on model training answer different questions. Review the chosen configuration to determine whether the complete request fits the intended boundary.

Control what reaches the prompt

The application should select context through the user's permitted access before sending it to the model. Prevent an unauthorized record from entering the request. Filtering it from the response would occur after the model had already received it.

Use governed views for structured questions. A headcount view can establish the population, date and permitted scope before a query runs. The model then works with a defined measure.

Document search needs comparable treatment. Results and excerpts may reveal information even if the full document remains restricted. Apply the reader's access to each part of retrieval.

Retrieved text contributes evidence to an answer. It must not change the application's permissions or authorize a tool. Keep control of those decisions in the application that handles the request.

Account for what the system retains

The data review must include the records left after the answer is returned. Identify saved conversations, uploads, caches and diagnostic records. Specify their readers and retention periods alongside the live request path.

Managed-service features can persist files or message history. Microsoft describes that storage separately from inference and service monitoring. Include the enabled features in the deployment review.

Choose what the application records for support. A request ID and failure status may be sufficient to locate an incident; storing a full result introduces another copy of the data. Retain what the investigation requires under appropriate access controls.

Deletion and recovery also need a defined scope. Establish which stores and backups contain the information and what evidence must remain available. These decisions complete the data lifecycle beyond the answer shown on screen.

Test the answer and its permissions

A private deployment still needs to demonstrate that its answers are correct enough for the intended work. Use questions whose answers the team can establish independently. Inspect the calculation or cited passage as well as the wording of the response.

Try the same question through accounts with different permissions. Each should receive only its allowed scope, including in follow-up questions and saved conversations. Test a request for a record outside that scope and inspect how the application handles it.

Ambiguous measures need a useful response too. If “staff” could mean employees, assignments or available capacity, the system should resolve the intended definition before calculating. Private hosting does not remove that business ambiguity.

Test unavailable sources and stale inputs. The recipient needs to recognize when the evidence is insufficient, while the operator needs enough information to resolve the failed request.

Operate the complete service

The rollout needs support for the data, the model connection and the people using the answer. A source change can affect a query; a model change can affect its explanation. Role changes can alter the context a user may receive.

Run the relevant tests again when those parts change. Preserve the configuration, instructions and evidence required to investigate a changed response. Show users how to inspect an answer and report a problem.

Databasin Private can run in the customer's Azure tenant. The platform and team support connections, models and governed reporting with rollout and ongoing support. The implementation can address the model route and business definitions together, following the answer into everyday use.

Example: answering a restricted workforce question

This illustrative request asks why last month's capacity changed within the manager's permitted department.

Test the same request with an account that lacks department access. That test examines the complete route through which information could reach the user.

Decisions before a private AI rollout

1. Define the boundaryEstablish the information required by the task and its permitted processing locations. Include source records, document excerpts and any reused conversation history.
2. Inspect the routeTrace a request through the application, endpoint and stores. Check each enabled feature against the data boundary required for the work.
3. Test restricted casesUse known questions and accounts with different access. Include stale sources, denied requests and ambiguous definitions in the release evaluation.
4. Assign service supportArrange source and model maintenance alongside user support. Specify the evidence retained for investigating a failed request or a disputed answer.

Sources