A local AI model can process the prompt and documents you give it on your device, but it does not automatically have access to every file there. Its practical reach depends on the app: enabled filesystem tools, integrations and server settings can extend access beyond the chat. Local inference describes where the model runs; it does not, by itself, guarantee that the surrounding app never stores data or connects to the internet.
What data can a local AI model access?
At a minimum, the model can use the message you enter and information the app supplies to it. If you use document chat or retrieval-augmented generation (RAG), the app can also provide text or passages from the documents you select. LM Studio says its local document chat processes documents on the machine when used with local models: LM Studio’s document-chat documentation.
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Beyond that, access depends on the interface and its configuration. A model does not gain general access to your computer merely because it runs locally. But an app can provide tools that let it read files or call other services. The llama.cpp server documentation describes optional direct filesystem access through its web UI and support for MCP servers, which can expose additional tools: llama.cpp server documentation. What those tools can reach depends on how they are implemented and the permissions of the process running them; there is no universal sandbox guarantee established for every local AI app.
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- Document chat: The app can supply content from documents you choose, often by retrieving relevant passages.
- Filesystem or other tools: Enabled tools may let the assistant read files or access capabilities beyond the selected chat materials.
Does local AI send your data to the internet?
Not necessarily. Local inference can run without sending prompts to a cloud model provider, but an app may still make network requests for other purposes. LM Studio says core functions—including chatting with downloaded models, document chat, and running a local server—can work without an internet connection. Its documentation also notes that requests may be needed for features such as model statistics and download options, including information from Hugging Face: LM Studio’s offline-use documentation.
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Ollama distinguishes local processing from its cloud-hosted models in its privacy policy: Ollama privacy policy. Check that your app is actually using a local model if keeping inference inputs on your device is the goal. “Local” does not settle separate questions such as whether chat history, extracted document text, indexes, or logs are stored, or whether optional app features contact online services. Review the current policy and settings for the specific app; available documentation does not establish a complete retention comparison across local AI products.
How to keep a local AI setup private
- Choose a local model for the task. Use a downloaded model and the app’s local inference option, and verify that the selected provider is not a cloud-hosted model. Product behavior varies; LM Studio describes its local model and document features as offline-capable, while Ollama separately documents its local and cloud offerings.
- Give the app only the material it needs. Select only the documents relevant to the question. Treat document chat as data processing, and find out where that app keeps history, extracted text, indexes, and logs.
- Disable unnecessary tools. Leave filesystem and MCP integrations off unless a task requires them. If the app permits scoping access, limit it to the specific files or directories needed. The benefit of this least-privilege approach follows from the fact that tools can expand what the assistant can reach; the available documentation does not establish one common permission model across apps.
- Check optional network features. If you need a session with no internet access, disconnect the device or otherwise verify its network behavior rather than assuming that local inference means the app never connects. Model discovery, downloads, updates, statistics, and other optional functions may need network access.
- Protect the device and stored files. Use your normal device security controls and download models and updates only from sources you trust. The cited product documentation does not establish that a particular model file is safe or compare encryption and operating-system protections.
Can a local AI server expose your setup to other devices?
It can, depending on how the server is configured. LM Studio documents serving models on localhost or the local network. A server bound to localhost is intended for connections from the same machine; a service bound to a network-facing address may be reachable by other devices, depending on network and firewall settings.
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- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
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The llama.cpp documentation includes a sample Docker invocation using --host 0.0.0.0. That is an example configuration, not a recommendation to make a server publicly accessible. Check the bind address and surrounding network controls, and expose the service only to the clients that need it.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesPermission prompts are platform-specific. Apple’s guidance says, “The first time a program accesses the local network, the system displays an alert asking the user to approve that access.” See Apple TN3179. This describes Apple platform behavior, not a general rule for every desktop operating system.
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What to check before using local AI with sensitive information
- Is the selected model running locally, or is the app sending inference to a cloud provider?
- Are document chat, filesystem tools, MCP integrations, or other capabilities enabled—and what can each access?
- Where does this app keep conversations, document extracts, indexes, and logs?
- Which features can make network requests, even when the model itself runs locally?
- If a server is running, which address is it bound to, and which devices can connect?
Answering these questions for the specific app and setup gives a more useful privacy picture than the label “local” alone.
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