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What “self-hosted” does—and doesn’t—protect
Self-hosting gives your organization control over the assistant’s server and, depending on your design, its database, storage, network placement, logs, backups, and model connections. It does not automatically keep all data inside your organization or make the service compliant. Open WebUI’s security documentation places responsibility for securing the environment, infrastructure, and configuration on the deploying organization.
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The key distinction is between the assistant application—the interface, user accounts, and related services—and the inference endpoint, which processes prompts and generates answers. A self-hosted interface can connect to a local or private model, or to a hosted API. When an external provider handles inference, prompts and relevant context, including material drawn from uploaded documents, may be sent to that provider. Review the provider’s terms and approve the data types it may receive rather than treating the interface’s hosting location as the whole privacy boundary.
Open WebUI’s “Chat Data Privacy & Encryption” documentation describes the organization as controlling the server, database, storage, model connections, logs, backups, and network placement in a self-hosted deployment. Those controls also define who may be able to access data: application and database administrators, host operators, people with access to backups, and any external model provider you configure.
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Map where prompts, files, and credentials go
Before installing, draw the path from the employee’s browser to the assistant and onward to every service it uses. Mark whether each component is inside your approved environment, which people or providers can administer it, and what information it handles. Include secondary copies: data does not live only on the chat screen.
- Identity and access: the identity provider, assistant accounts, roles, and groups that determine who can sign in and which models or features they can use.
- Inference: the model server or hosted API, plus the prompts and document context sent to it.
- Document processing: upload handling, knowledge collections, and any vector store or other index used to retrieve content.
- Persistence: chat history, application database, persistent volumes, exports, and object storage.
- Operations: application and infrastructure logs, audit trails, traces, credentials, backups, and administrators with access to them.
For each path, decide whether it may handle confidential information. If you cannot establish where a component sends or retains data, do not approve it for sensitive workloads until you can verify its behavior.
Choose the model boundary before choosing the hosting pattern
| Inference choice | Data boundary | Decision to make |
|---|---|---|
| Local or private model endpoint | Prompts can remain within the organization only if the model runtime and network path are also controlled. | Confirm where the model runs, which systems can reach it, and who operates the host and model service. |
| Hosted model API | The provider receives prompts and context sent for inference; the assistant interface may still be hosted by your organization. | Assess the provider’s terms and approve which categories of company data may be sent. Route sensitive use only to an approved endpoint. |
“Private” is not a synonym for “local”: a privately operated endpoint may still be outside the company boundary. Treat the actual network route, operator access, and provider relationship as part of the decision. Ollama’s privacy policy also distinguishes local processing from requests to cloud-hosted models, so check which type of endpoint your deployment uses.
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Pick storage and scaling to match the deployment
Open WebUI’s Kubernetes guidance describes two broad application-storage patterns. The application pods connect to a model server or API configured separately; the assistant’s UI deployment does not, by itself, determine where inference runs.
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| Pattern | Documented shape | What to account for |
|---|---|---|
| Simpler installation or pilot | One persistent application replica, with storage suitable for SQLite. | Protect the persistent data volume and plan how it will be backed up, retained, and restored. |
| Multiple replicas or needs beyond SQLite | Shared PostgreSQL, Redis, and object storage in the documented production pattern. | Include each shared service, its operators, storage, logs, and backups in the access and retention design. |
This is an architectural distinction, not a universal sizing recommendation. The cited Kubernetes guidance does not establish hardware requirements for a particular workload. Choose capacity and release versions from current official deployment documentation rather than inferring them from this pattern.
Control who can sign in and what they can do
Connect the assistant to the organization’s identity provider where supported, and enforce multifactor authentication through that provider. Open WebUI’s enterprise information says its own password login does not have built-in MFA; do not assume a local password screen supplies a second factor.
- Use narrowly scoped roles and grant access to models by group where available.
- Limit administrator access to chats and exports to the people and purposes that require it.
- Restrict document uploads, knowledge collections, tools and functions, direct connections, and API-key use to approved users or groups.
- Govern chat or content sharing so users do not inadvertently make sensitive material available more broadly.
Separate deployments or databases may be appropriate where departments have different sensitivity or operator requirements. That separation only helps if the accounts, administrators, storage, and supporting services are separated as intended; document who can cross each boundary.
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Protect stored data, logs, and secrets
Chat history and uploaded material can persist in database files, persistent volumes, exports, object storage, logs, traces, and backups. Inventory these locations and define retention and deletion or archival practices for each. Encrypt production database storage and backups; for SQLite, use encrypted filesystem storage. Encryption at rest helps protect lost or copied media, but it does not remove live application, database, host, or secret operators from the trust boundary.
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Keep signing keys and model API keys out of source control. Use a secret manager or an equivalent protected mechanism. In Kubernetes, restrict access to Secret objects with role-based access control and enable encryption at rest for those secrets. Limit who can read, modify, or export them.
Prefer metadata-only audit logs unless the logging platform is specifically approved to hold chat content. Logs and traces can reproduce sensitive information even when the main database is well protected. Set retention for forwarded logs and verify what the application and infrastructure actually record before enabling real user data.
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Keep access within a private trusted network where practical, using a VPN, zero-trust access proxy, or authenticated reverse proxy with IP allowlisting. Apply rate limiting and controls against brute-force attempts at the network or proxy layer. Use official images or build from source, monitor authentication, and audit access to the service and its infrastructure.
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Network controls should match the data-flow map: restrict which systems can reach the assistant, which endpoints it can call, and where uploaded or generated data can leave. An internal login page is not a substitute for controlling those routes.
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Pilot safely and verify the controls
Start with non-sensitive data. Before approving company information, test the deployment against the boundaries you documented:
- Confirm the endpoint: verify the configured model server or API and inspect network egress to ensure prompts go only to approved destinations.
- Test identity and permissions: confirm authentication, MFA through the identity provider, role boundaries, model access by group, and restrictions on administration, uploads, tools, sharing, and exports.
- Inspect persistence and observability: check where chats, files, indexes, logs, and traces appear; verify retention and deletion or archival behavior.
- Test recovery: verify backup encryption and perform a restore test using the organization’s approved procedures.
- Exercise operations: document update and rollback procedures, monitor authentication, and review who can access application data, infrastructure, secrets, and backups.
Do not treat the existence of a control setting as proof that it works as intended. Approval should depend on observed behavior in your deployment and on a clear record of what data is retained, where it can travel, and who can access it.
Know what the documentation can establish
Open WebUI’s deployment and security documentation provides configuration guidance, not an independent security audit or a guarantee that a particular deployment is secure. The sources cited here do not establish a universal hardware-sizing figure or vendor-neutral certification. Your organization’s security and compliance review still needs to assess the actual model provider, infrastructure, identity setup, retention rules, and operating practices.
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