# Private LLM Hosting: What Security Teams Need to Check Canonical: https://blog.r2copilot.ai/articles/private-llm-hosting-what-security-teams-need-to-check/ Author: R2 Copilot Journal Edition: 2026-10-15T09:00:00Z Published: 2026-10-16T23:05:45.644Z Updated: 2026-10-16T23:05:45.644Z ![An illustrative map separating the user, application, model and retained data boundaries](https://blog.r2copilot.ai/assets/R2-BLOG-001/hero.459be7eed5b0.png) Private LLM hosting requires a specified data boundary. Before approving a workflow, check who can connect, where requests are processed, what data is retained and which evidence confirms the controls work for the actual configuration your team uses. ## Start with the Workflow, Then Draw Its Boundaries A hosting label tells you little about the path a document takes. Begin with one task: for example, summarising an internal policy for an employee. This is a hypothetical workflow, not an account of a customer deployment. List the file source, application, model endpoint and destination of the answer. Add any search index or conversation store the design uses. Now assign an owner to each boundary. The application team may control what enters a prompt, while another team operates the network and identity settings. A provider may process the request under its own service terms. These responsibilities need to be explicit before anyone approves sensitive information for the workflow. When reviewing private AI deployment options, compare what the organisation actually controls. A dedicated environment, a private network connection and locally operated software answer different questions. Ask for the architecture and operating responsibilities behind each proposal rather than treating those labels as equivalent. ## Check Network Access Separately from Data Handling A private connection is one part of the assessment. In its [network isolation guidance](https://learn.microsoft.com/en-us/azure/foundry/how-to/configure-private-link), Microsoft separates inbound and outbound access and explains private endpoint and DNS checks. That distinction is useful beyond a single platform: reaching the model privately does not describe every connection used by the surrounding application. Turn private AI infrastructure requirements into a short evidence list: - Which users and application identities can submit requests? - Which external destinations can the application reach? - How are connections to document stores and search services configured? - Who can change network settings, and how are changes reviewed? - What test confirms the intended endpoint and name resolution? Ask the responsible engineer to show the relevant configuration and a bounded connectivity check. A diagram describes intent; a recorded check describes what was observed. Neither should be stretched into a claim that every possible data path has been tested. ![A checklist separating inbound identity, outbound destinations and configuration evidence](https://blog.r2copilot.ai/assets/R2-BLOG-001/network-checks.f76e6e7c581c.png) ## Separate Processing Location from Stored Data If you plan to use a private LLM on company data, identify exactly what is sent with each request. A retrieved passage, uploaded file and stored conversation may follow different handling rules. Record which features the workflow enables rather than relying on a general privacy statement for an entire product family. Microsoft's [model data-handling documentation](https://learn.microsoft.com/en-us/azure/foundry/responsible-ai/openai/data-privacy) distinguishes inference, stateful storage and deployment-dependent processing locations. Its commitments apply to the specified Azure services and features; they should not be assumed for another vendor or arrangement. Use that distinction to ask better questions, then inspect the terms that govern your own setup. For each relevant data type, record its processing location, storage location, permitted access and deletion conditions. Include application logs and connected services in your assessment. If an answer is unclear, write down the missing evidence and the person responsible for obtaining it. A blank field is more useful than an unsupported assurance. ## Make the Pilot Prove a Bounded Decision A private AI pilot for enterprises should have a defined task and acceptance criteria before it starts. Choose an approved test dataset, a small participant group and an output that a reviewer can assess. Keep the first decision narrow: whether this workflow is suitable to proceed under the documented conditions. The pilot record should connect each requirement to evidence: - Access: show which test identities are permitted and how an unauthorised identity is rejected. - Data scope: confirm the approved inputs and inspect what the application sends. - Retention: identify the configured stores and demonstrate the applicable deletion process. - Answer quality: have a qualified reviewer check outputs against the source material. - Ownership: name who resolves a failed check and who decides whether the pilot continues. These are proposed assessment steps, not results of a test performed here. Define the expected observation and its limits. A successful check for one identity or document does not establish behaviour for every user, integration or workload. ![An illustrative pilot sequence from approved inputs through checks to a go, revise or stop decision](https://blog.r2copilot.ai/assets/R2-BLOG-001/pilot-decision.88dcbae61c3a.png) ## End with an Evidence Record, Not a Hosting Label Keep the final assessment brief enough for a decision owner to use: the workflow, data boundary, responsibilities, observed checks and unresolved conditions. Separate what was configured, what was tested and what still depends on documentation or an external commitment. Update the record when the workflow or enabled features change. If your team is evaluating R2 Copilot, use this checklist to frame the questions you take into the product discussion. It describes an assessment method, not a statement that R2 Copilot implements every control listed above. Your next step is to draw one real workflow and assign an owner to each unanswered boundary question. That gives security and implementation teams a concrete basis for deciding what evidence they need before expanding access.