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Running a model on your own computer protects only the request that actually reaches that local model endpoint. A connected tool, remote MCP server, file parser, embedding API, search service, hosted application, database, log, export, or backup can receive the same prompt or file independently.
Privacy therefore depends on the entire data path, not the model label. Map every destination, keep sensitive work on local or private endpoints, minimize permissions, require approval for consequential actions, and secure every stored copy.
What “local” protects—and what it does not
Inference location is the first privacy boundary. Open WebUI documentation states, “The selected endpoint determines where inference happens.” When Open WebUI is configured for a local provider, the model request and the context sent to that endpoint can stay within your device or network. Selecting a hosted model sends the prompt and included context to that provider instead. If you compare several models, the same prompt may be sent to every selected endpoint.
That boundary does not automatically include connected capabilities. A local model can still call a remote MCP server, upload a document to a cloud extraction service, create embeddings through a hosted API, query a search connector, or store chat history in a database managed by somebody else. Each destination has its own operators, retention period, access controls, and data-residency rules.
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Five separate privacy questions
- Where is inference performed? On the local device, private organizational infrastructure, or a hosted provider?
- Where else is content processed? Check tool servers, MCP connectors, parsers, OCR, embedding, search, moderation, and other secondary services.
- What is retained? Consider the original upload, extracted text, embeddings, chat record, logs, exports, caches, and backups.
- Who can read or change it? Include application administrators, database administrators, host operators, backup custodians, connector owners, and anyone holding live service credentials.
- What can the AI do? Reading a file is a different risk from modifying a document, sending a message, deleting data, or running a server-side function.
Ollama describes a defined local-runtime boundary in its privacy policy: “We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” The statement applies to content processed locally with Ollama. It does not make a separately configured connector, extraction service, embedding endpoint, or hosted application local.
A practical data-flow inventory
| Component | May receive | Questions to answer |
|---|---|---|
| Interface or chat application | Prompts, selected files, conversation history, user identity | Where is it hosted? Who administers it? Is history encrypted? |
| Model endpoint | Prompt, system instructions, retrieved passages, tool results | Is it local, private, or hosted? What provider terms apply? |
| File extraction or OCR | Original file or page images and extracted text | Does extraction run locally? Are originals or text retained? |
| Embedding service | Chunks of document text and sometimes metadata | Is the endpoint external? Where are vectors stored and deleted? |
| Tool or MCP server | Arguments, retrieved records, account identifiers, tool results | Who operates it? What systems can it access? Does it retain requests? |
| Application database | Chats, file metadata, extracted content, embeddings, permissions | Which administrators and hosts can access it? Is storage encrypted? |
| Logs, exports, and backups | Operational events or complete conversation and file content | Are logs metadata-only? Are copies encrypted and covered by deletion policy? |
Map the path before sending a sensitive file
Do this inventory before enabling a connector, not after a confidential document has already been uploaded.
- Record the entry point. Write down the application, workspace, account, and group that will handle the conversation.
- Identify every model endpoint. Note the exact provider selected for generation, summarization, vision, fallback, and model comparison. A local default does not prove that every feature uses the same endpoint.
- List secondary processors. Include file parsing, OCR, chunking, embedding, retrieval, search, moderation, speech, and translation services.
- List tools and MCP servers. For each tool, record the host, operator, permissions, arguments sent, systems reached, and stated retention or residency terms.
- Trace persistence. Find the chat database, uploaded originals, extracted text, vector store, caches, application logs, administrator exports, and backup repositories.
- Document revocation. Specify how to remove a user, revoke a token, disable a connector, delete a file, rotate a secret, and retire a backup.
- Test with non-sensitive data. Inspect network routes, application records, tool logs, and storage locations before permitting production content.
Keep the map current. Adding a vision model, changing a parser, enabling a knowledge base, or switching a connector can introduce a new recipient without changing the chat screen.
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Choose endpoints according to data sensitivity
Use the least exposed architecture that still meets the task. The right choice depends on the information involved, your threat model, and whether your team can operate the required services reliably.
| Data category | Preferred arrangement | Additional controls |
|---|---|---|
| Public or already-published material | Local or hosted inference may both be acceptable | Still review connector permissions and retention because credentials and personal data can be exposed. |
| Internal business information | Local or private organizational model and private storage | Restrict cloud models by group; encrypt databases and backups; review administrator access. |
| Confidential customer, legal, health, or source-code data | Local or private endpoints for inference, extraction, and embeddings | Keep remote tools out unless their provider, residency, retention, and permissions are explicitly approved. |
| Highly restricted or regulated material | Isolated deployment with dedicated operators, credentials, storage, and backups | Use network isolation, strong access review, approved tools only, and an auditable deletion process. |
Disable Ollama cloud features when local-only operation is required
Ollama documents a product-specific local-only setting. Set disable_ollama_cloud in the Ollama server configuration or set the environment variable OLLAMA_NO_CLOUD=1, then restart Ollama. Its documentation says this removes access to Ollama cloud models and web search.
This is a control for Ollama, not a network-wide privacy guarantee. Open WebUI, an MCP server, a file extractor, an embedding provider, or another application can still send data elsewhere. Verify the endpoint used by each feature after changing the setting, and expect cloud-only models and web search to disappear.
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Do not infer privacy from the word “self-hosted”
A self-hosted application can still be readable by the people who run it. The live application process, database administrators, host operators, log collectors, export recipients, and backup custodians may all sit inside the trust boundary. Separate deployments, databases, credentials, backups, operators, and model providers can create a stronger boundary between teams with different confidentiality requirements.
Limit tools and MCP servers
A connector is another recipient, even when the model itself is local. Remote MCP servers are third parties with their own retention and data-residency policies. A connector may expose information to the service it connects to, so assess the connector host and its downstream service separately from the model provider.
Approve a tool in four passes
- Inspect the owner and host. Prefer an official server when one exists, but still review its behavior, update process, and terms. Identify the legal entity and infrastructure operating a remote server.
- Read the tool definitions. Determine exactly what arguments are sent, whether the tool can retrieve broad collections, and whether it forwards data to another API.
- Reduce permissions. Use read-only scopes where possible, limit repositories, folders, records, and accounts, and give access only to users or groups that need it.
- Require approval for impact. Put human confirmation in front of writes, deletes, messages, purchases, permission changes, code execution, and other irreversible or externally visible actions.
Keep upload, knowledge-base, and server-side function permissions off by default for groups that do not need them. Give administrators a separate workflow for testing new servers rather than exposing an unreviewed connector to every user.
Treat retrieved documents as untrusted input
A document can contain instructions aimed at the model rather than information you intended to retrieve. This is prompt injection: hostile text attempts to change the model’s behavior, reveal secrets, or trigger a tool call. Local inference does not remove that risk.
- Separate document content from system instructions and tool policy.
- Do not let retrieved text grant itself new permissions.
- Show tool arguments and outputs to a reviewer for sensitive workflows.
- Constrain destinations, record types, commands, and file paths with allowlists.
- Use dry-run or preview modes before applying changes.
- Assume a compromised document can be both confidential and adversarial.
OpenAI’s MCP documentation warns, “MCP servers may update tool behavior unexpectedly, potentially leading to unintended or malicious behavior.” Treat a server update as a security change: review the new tool list and permissions before publishing it.
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OpenAI’s administrator guidance for MCP-enabled workspaces says administrators can refresh tool lists, while ChatGPT uses a frozen snapshot of approved tools until an administrator reviews and publishes an update. This means a connector’s current behavior and the approved behavior may differ after an update. Establish a recurring review and republish process rather than assuming approval is permanent.
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Control the complete file lifecycle
Uploading a file creates more than one copy. The application may preserve the original, extract text or images, split content into chunks, generate embeddings, save metadata, add passages to a knowledge base, write logs, and include content in exports or backups.
Set restrictive defaults
- Disable uploads and knowledge access for groups that do not need them.
- Accept only required file types and sizes; reject archives or executable content unless there is a documented reason.
- Use separate knowledge bases for different teams and confidentiality levels.
- Apply the narrowest folder, repository, or record scope to retrieval.
- Do not place secrets in filenames, metadata, prompts, or tool arguments.
Delete every copy you control
Open WebUI’s FAQ says users can review and delete uploaded files in File Manager. It also says deleting a file cleans associated Knowledge Base entries and vector embeddings. That is useful application-level cleanup, but it does not prove that independent backups, exports, downstream connectors, or provider-side retention have been erased. Check each system’s deletion and retention behavior separately.
When a sensitive file must be removed, delete it from the application, remove derived knowledge-base records, revoke links and tokens, purge approved logs and exports, and identify backup copies that remain under their retention schedule. Record the completion date and the person responsible.
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Secure databases, logs, backups, and operators
Open WebUI’s published production guidance recommends PostgreSQL with encrypted storage, TLS, encrypted backups, and network isolation. For SQLite-only deployments it recommends an encrypted filesystem. Encryption at rest protects disks and backup media; it does not prevent a running application or an authorized database administrator from reading plaintext data.
Use layered storage controls
- Transport: Use TLS between browsers, application servers, databases, model endpoints, and remote tools.
- At rest: Encrypt database volumes, file stores, vector stores, removable media, and backup repositories.
- Keys: Keep encryption keys separate from the data they protect, restrict key access, and rotate credentials after staff or service changes.
- Network: Isolate the application and database, permit outbound connections only to approved endpoints, and block unexpected egress.
- Backups: Encrypt them, limit who can restore them, test restoration securely, and define their retention period.
- Access: Use unique administrator accounts, multifactor authentication where available, short-lived service credentials, and periodic access reviews.
Prefer metadata-only audit logs
Logs can quietly become a second chat archive. Unless the organization has approved full-content logging, record metadata such as user, timestamp, endpoint, tool name, decision, and result status without copying prompts, file contents, or tool payloads. If full content is necessary for an approved investigation, restrict access, encrypt it, set an expiry, and include it in the deletion plan.
Understand the operator boundary
Self-hosting changes who you trust; it does not eliminate trust. Administrators who can inspect the live process, database, host memory, logs, exports, or backup keys may be able to access conversations and files. For teams that cannot share that access, use separate infrastructure and operators, not merely separate user accounts.
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Three workable deployment patterns
| Pattern | Architecture | Best fit | Main limitation |
|---|---|---|---|
| Personal local workspace | Local model, local extraction and embeddings, encrypted local storage, no remote tools | Private notes, drafts, and offline experimentation | Limited collaboration and fewer connected capabilities |
| Private team workspace | Model and application on organization-controlled infrastructure, encrypted database and backups, approved internal tools | Internal documents and controlled collaboration | Requires patching, monitoring, access reviews, and incident response |
| Isolated high-sensitivity workspace | Dedicated deployment, network egress restrictions, separate operators and credentials, local processing throughout | Regulated or exceptionally confidential material | Highest operational cost and the narrowest tool selection |
A cloud model can be appropriate for lower-sensitivity work when its terms and controls meet your policy. Do not treat a provider’s no-training statement as proof that a non-synced connector, parser, or MCP server has the same policy.
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OpenAI states that Business, Enterprise, and Edu workspace content, including information accessed through apps, is not used to train its models by default. That statement applies to the specified workspace products. It does not determine how a non-synced third-party app stores or processes information sent to it.
OpenAI also states that information sent to connected providers is subject to those providers’ terms. Its MCP documentation places data sent to MCP servers under the servers’ own retention and residency policies. Therefore, evaluate the workspace, the connected app, and the MCP host as separate processing destinations.
Recheck privacy after every meaningful change
Make endpoint and connector review part of change management. Recheck before sending sensitive content when any of the following occurs:
- A model, fallback provider, vision model, parser, or embedding endpoint changes.
- A new MCP server or tool definition is added, removed, or updated.
- A workspace, user group, knowledge base, or file permission changes.
- The application, database, vector store, logging pipeline, or backup destination moves.
- Cloud features are enabled, disabled, or restored after an upgrade.
- A service credential, encryption key, administrator, or hosting provider changes.
For each change, refresh the tool list, inspect outbound network destinations, run a harmless test document through the complete path, verify storage and deletion behavior, and document the resulting architecture.
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“The model is local, but the provider dashboard shows traffic”
Check whether the interface selected a hosted fallback, sent prompts to multiple models for comparison, or used a cloud-only feature such as web search. Inspect the active endpoint in the application and disable unneeded cloud options.
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“A file was deleted, but storage usage did not fall”
Look for extracted text, embeddings, knowledge-base records, exports, caches, snapshots, and backups. Application deletion may clean its own records while independent systems retain copies until their scheduled purge.
“We disabled cloud inference, but a connector still receives data”
Cloud-model settings do not govern remote MCP servers, external parsers, embedding APIs, or search tools. Disable or reconfigure each service, then test network egress and tool logs with a harmless marker.
“A tool performed an action without the user expecting it”
Reduce the tool to read-only access, add explicit approval for writes and deletes, constrain destinations with allowlists, and review recent tool-definition changes. Treat retrieved document instructions as untrusted.
“Administrators can see conversations even though the model is local”
Review application roles, database access, host privileges, log destinations, export permissions, and backup keys. If that access is unacceptable, create a separate deployment with independent operators and storage.
A concise pre-send checklist
- The selected model endpoint is approved for this data.
- File extraction and embedding run on approved infrastructure.
- No unapproved MCP server, search connector, or hosted tool is enabled.
- Uploads and knowledge bases expose only the necessary folders or records.
- Write, delete, message, code-execution, and permission-changing actions require confirmation.
- Database, file, vector, log, export, and backup storage is encrypted.
- Outbound network access is limited to documented destinations.
- Operators and administrators are inside the intended trust boundary.
- Deletion and backup-retention procedures have been tested.
- The endpoint and tool list were rechecked after the last configuration change.
Privacy is strongest when the entire route is intentional: local or private inference, approved secondary processors, minimal permissions, reviewed actions, encrypted storage, and operators you have explicitly chosen to trust.
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