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Local AI can keep a chat’s inference on your device, but “local” does not automatically mean that every prompt, file, index, log, or backup stays there. Privacy depends on the full path: the chat interface, selected model endpoint, file-processing services, connected tools, storage, and everyone with access to those systems.
Before sharing anything sensitive, verify which model and services the chat will use. Then secure the data that remains on your device or server, including chat history, uploaded files, indexes, logs, and backups.
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Does local AI send your chats online?
It depends on the model endpoint and the other services your app uses. A local model can process a prompt on your device, while a cloud model sends the prompt and included context to the provider. Open WebUI describes this as endpoint-dependent: “The selected endpoint determines where inference happens.” Open WebUI’s guide to connecting local and cloud models says a message goes to the configured provider endpoint.
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Check the route for each sensitive chat
- Identify the chat interface, model runtime, selected model, and any file-indexing component. Each may have different network and storage behavior.
- Confirm the current chat’s model and configured endpoint. If it is a cloud model, treat the prompt and any included context as information sent to that provider under its terms.
- Review enabled search, extraction, embedding, tools, and agent features. Disable services you do not need for sensitive work.
Where are local AI chats and files stored?
Local or self-hosted data may include chat histories, uploaded documents, file indexes, databases, application logs, and backup copies. Keeping them off a provider’s cloud does not make them inaccessible: other users of the device, application administrators, database operators, host administrators, or backup operators may be able to reach them, depending on the setup and permissions.
Open WebUI emphasizes that a self-hosted deployment puts the server, database, storage, model connections, logs, backups, and network placement under the organization’s control. That control also means the organization must secure them. Its privacy documentation notes that the running application and database key remain inside the trust boundary; encryption at rest does not prevent access by a running app or a sufficiently privileged operator. See Open WebUI’s chat data privacy and encryption guidance.
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Protect active data and backup copies
Use a strong device login and supported full-disk encryption. For Open WebUI’s SQLite setup, its current guidance recommends putting the data directory on an encrypted filesystem such as FileVault, BitLocker, or LUKS. It says SQLCipher has been unsupported since v0.9.0, so do not assume app-level database encryption is available or enabled by default.
Encrypt backup copies separately and test that you can restore them. An encrypted external drive can be a backup destination, but it does not protect active files while the drive is mounted or prevent a cloud service from receiving a prompt.
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How to limit access, retention, and logs
Use the controls your application provides to reduce routine access to chat content. In Open WebUI, documented controls include temporary chats, settings for administrator chat access and exports, and group-scoped permissions for files and knowledge. These are product-level controls, not a barrier against a trusted host or operator with access to the underlying infrastructure.
- Choose temporary chats or shorter retention where available and appropriate.
- Review who can access chat histories, export conversations, upload files, or share knowledge collections.
- Keep audit logs metadata-only unless storing chat bodies is explicitly approved. Logs can otherwise become another copy of sensitive content.
- When deleting a chat or file, check whether the product also removes related indexes, exports, logs, and backups; deletion behavior varies by product.
What can plugins and tools access?
Treat extensions and tools as code with permissions, not as harmless add-ons. Open WebUI’s hardening guide says server-side Tools and Functions can read files, make network requests, access environment variables, and interact with the database under the permissions of the application process. A local model does not constrain what an enabled tool can do. See Open WebUI’s security hardening guide.
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- Enable only tools and functions you trust and understand.
- Restrict who can create, import, or modify them.
- For self-hosted services reachable over a network, use access controls and TLS where appropriate, restrict allowed origins, and do not expose an unauthenticated service to the internet. Open WebUI documents a wildcard CORS default and advises restricting origins.
What do local AI vendors say about privacy?
Vendor policies are useful for understanding a product’s stated behavior, but they are not independent audits and should not be generalized to other apps or configurations. Read them feature by feature: local inference, downloads, update checks, cloud features, and telemetry can have different data flows.
| Product | Vendor’s stated behavior | What to keep in mind |
|---|---|---|
| LM Studio | LM Studio says prompts, histories, and documents used with local models stay on the device. Its privacy documentation separately describes network activity for model downloads, update checks, and cloud features. | These claims concern LM Studio’s described features; check whether a cloud feature or another configured service is involved. |
| Ollama | Ollama says it does not collect or transmit local prompts and responses, while it may collect limited non-content device and usage metadata. It says prompts sent to cloud-hosted models are handled transiently. See Ollama’s privacy policy. | This is the vendor’s policy statement, not an independent verification. Local and cloud-hosted use have different data paths. |
| Open WebUI | Open WebUI says messages go to the configured provider endpoint; its self-hosting documentation describes the operator’s control over storage, logs, backups, model connections, and network placement. See its privacy guidance and model connection documentation. | The selected endpoint and enabled tools determine where content can go. Self-hosting places responsibility for securing the deployment on its operator. |
Recheck the relevant privacy policy and configuration after significant updates: cloud features, defaults, and product behavior can change.
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- 【Two Attachment Options】- Installs in minutes. Option 1 uses clear adhesive strips that securely attach to any screen. Option 2 uses slide mount tabs that easily stick to the display frame, allowing you to slide the filter on and off the screen as needed.
- 【Superior Privacy and Reduce Glare】- Our advanced multi-layered film filter blacks out your screen when viewing from the side, while maintaining a crystal clear screen straight-on. It also protects your eyes from harmful glare, UV, and blue light. [Note: It does not block visibility directly behind you, regardless of the distance.]
- 【Perfect for Travel and Open Workspaces】- Our computer screen privacy filter is the ideal solution for healthcare providers, mobile workers, commuters, students, and business travelers. Now you can stay compliant and safeguard sensitive corporate information while working in airplanes, subways, airports and public areas.
- 【Package Contents】- Each package includes one privacy screen shield filter, two sets of clear adhesive strips, two sets of slide mount tabs, and a microfiber cleaning cloth. Buy with confidence – located in the US, Sight Pro specializes in providing best-in-class privacy solutions to individuals, small businesses, corporations, government, and educational institutions. Our privacy screens are Section 889 and TAA compliant.
How to assess a setup before using it
Compare the parts of the data path rather than relying on a “local” label. For each candidate setup, establish:
- Which endpoint performs inference and what data terms apply there.
- Where chats, files, indexes, and logs are stored.
- Whether encryption covers active storage and backups.
- Who has application, database, host, and backup access.
- What retention, deletion, export, and sharing controls are available.
- Which connected tools or services can read or transmit content.
There is no universal privacy ranking supported across local AI applications: the answer depends on the product, configuration, hosting arrangement, and people with access. The cited vendor statements describe their own services, not every extension or deployment. They also do not independently establish how every platform stores files or behaves on the network.
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