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Local AI is private only when the entire request path stays on your device. A downloaded model can analyze research notes or source code without sending prompt content to a provider, but cloud fallbacks, web-search tools, remote connectors, exposed APIs, telemetry, and compromised computers can still disclose information. The practical approach is to define what you are protecting, install models deliberately, disable cloud routes, verify that retrieval and embedding components are local, and keep the inference endpoint reachable only where necessary. This guide explains how to do that with local runtimes such as Ollama and LM Studio, how their documented privacy boundaries differ from hosted confidential inference, and how to check for residual network and metadata risks.
What does “private” mean when you run an AI model locally?
Local inference means the model weights and the computation for a request run on hardware you control. In that narrow path, the prompt, response, attached files, and source code do not need to cross an AI provider’s service. Privacy is not automatic, however: downloading a model, checking for updates, searching for models, using web search, calling a connector, or routing a request to a hosted model creates a different data boundary.
Before installing anything, write down the asset and adversary you care about. Your threat model may include:
- An AI provider retaining prompts, documents, or generated code.
- Network observers seeing requests or metadata.
- Other users on the same computer opening local files or API endpoints.
- Malware, a malicious browser extension, or a compromised developer tool reading the workspace.
- An accidental cloud fallback, remote MCP server, web-search feature, or hosted embedding service.
- Logs, crash reports, support tickets, model names, IP addresses, or timestamps that reveal sensitive work even when prompt text remains local.
Local inference primarily addresses provider access during a local request. It does not make a compromised endpoint safe, encrypt files at rest, or prevent a connector from receiving data you deliberately send to it.
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Does local inference keep prompts and code away from the provider?
Yes, when the selected endpoint is genuinely local and no external feature receives the content. Ollama’s privacy policy says it does not collect, store, transmit, or access prompts, responses, model interactions, or other content processed locally. That statement applies to local processing, not to Ollama’s cloud-hosted path, which has a separate data boundary.
LM Studio documents a similar offline boundary: after model files are present, its downloaded models, document chat or retrieval-augmented generation (RAG), and local server can operate without an internet connection. Its documentation states that content entered while chatting with language models does not leave the device and that a document dragged into the application for chat or RAG stays on the machine. Those guarantees depend on using the offline, local path rather than an optional cloud service.
What local processing does protect
- The text of a prompt sent to a local endpoint.
- Documents indexed by a local RAG workflow, if the model, embedding component, and index are local.
- Source files supplied to a local coding assistant, provided its extensions do not forward them elsewhere.
What it does not protect
- Files copied to a remote repository, issue tracker, cloud drive, browser search, or support email.
- Data exposed by malware, an untrusted plugin, or another account on the computer.
- Requests sent through a hosted model, remote connector, or cloud fallback.
- Metadata generated by downloads, updates, account activity, diagnostics, and network access.
How should I define a threat model before installing a model?
Start with the consequence of disclosure, then decide which paths are acceptable. A hobby project may tolerate model-download metadata; unreleased source code, legal documents, or regulated records may require a disconnected workstation and encrypted storage.
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Ask four boundary questions
- Where may content run? Restrict sensitive prompts and files to a named computer, or permit a managed hosted service only when its execution and retention terms meet your requirements.
- Who may reach the runtime? Decide whether only your account, other local users, a development team, or a remote network may call the model API.
- Which metadata is sensitive? Model names, download times, IP addresses, update checks, logs, crash reports, and support conversations can reveal a project even when prompt text stays local.
- What happens if the endpoint is compromised? Local inference cannot defend against malware with access to your files, process memory, browser, or API credentials.
Record these decisions before setup. They determine whether you can work offline, whether the API must remain loopback-only, and whether optional search or connector features must be removed rather than merely ignored.
How do I install and download models without creating unnecessary exposure?
Download model weights while connected to a network you trust, verify the model name and source, and then disconnect for sensitive work when practical. The download phase is not the same as inference: model searches, weight downloads, update checks, and account operations can contact external services.
- Choose the runtime and model deliberately. Record the runtime version, model identifier, quantization or file format, and the source from which you obtained the weights.
- Download before handling confidential material. Complete model and runtime downloads while the machine is online. Do not paste private documents into a model-search or setup form.
- Save an inventory. Note where model files, indexes, prompt histories, logs, and configuration files are stored. Apply filesystem permissions appropriate to the sensitivity of the work.
- Disconnect for the sensitive session. Airplane mode or a firewall rule can reduce network paths, but confirm that your application still starts and that all required model and embedding files are already local.
- Reconnect only for a planned maintenance task. Treat updates and additional model downloads as separate, reviewable operations rather than background activity during research.
Offline operation is a capability, not proof that every installation is offline. A browser extension, plugin, or separate indexing service can still transmit content unless you check its configuration.
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How do I disable Ollama cloud features?
Ollama documents two ways to disable its cloud path. Set the environment variable OLLAMA_NO_CLOUD=1, or set disable_ollama_cloud to true in ~/.ollama/server.json. Restart Ollama after changing either setting so the running server loads the new policy.
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- Stop the Ollama service or application.
- Choose one documented control: export
OLLAMA_NO_CLOUD=1in the service’s environment, or edit~/.ollama/server.jsonand add or change"disable_ollama_cloud": true. - Start Ollama again.
- Confirm that cloud models and web-search routes are no longer offered by the client you use.
- Inspect the client’s configured base URL. A local request uses
http://localhost:11434/api; a hosted endpoint is a different destination and requires an API key.
Do not assume that disabling a menu item in a graphical client changes the runtime. Check both the application setting and the server configuration, especially when an editor extension or another program can supply its own endpoint.
How can I keep research documents private with local RAG?
Use a local retrieval pipeline in which document parsing, chunking, embedding, index storage, and generation all occur on the device. LM Studio explicitly documents offline document chat and RAG, with attached documents staying on the machine. Other clients may delegate embeddings or indexing to a hosted service, so verify each component rather than relying on the word “local” in the interface.
Local RAG checklist
- Store the original files and the vector or keyword index on an encrypted local disk.
- Confirm which embedding model creates the index and where that model runs.
- Check that OCR, PDF extraction, spreadsheet parsing, and other preprocessors do not upload files.
- Disable browser search, remote MCP servers, cloud connectors, and automatic fallback models for the workspace.
- Use a test document containing a harmless unique phrase, then monitor the application and firewall while indexing and querying it.
- Delete temporary extracts, caches, and indexes when the project ends; retention is a local file-management issue even when no provider sees the content.
RAG can reduce what you paste into a prompt, but it does not change the trustworthiness of the host computer. A local index readable by another account or malware is still a disclosure.
How do I keep a local AI API from becoming a network leak?
Keep the inference server on loopback unless remote access is required. Ollama’s documented default is 127.0.0.1:11434. That binding limits ordinary network access to the same machine; changing the host binding or placing the service behind a proxy broadens the attack surface.
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For a single developer using a local editor or command-line client, leave the default loopback binding in place. Make sure the editor points to the local URL, not a hosted URL, and avoid copying an API key intended for a cloud service into a local configuration.
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When a team needs remote access
- Put the service on a restricted network segment rather than the public internet.
- Allow only named client addresses through the firewall.
- Require authentication at the proxy or gateway; do not expose an unauthenticated inference endpoint.
- Terminate TLS at a properly managed proxy so credentials and prompts are protected in transit.
- Log access without logging prompt bodies unless there is a documented need.
- Review the changed
OLLAMA_HOSTsetting and proxy rules after upgrades, because the operator’s configuration determines the final exposure.
Remote access also changes the threat model for every document sent to the server. A private model on a server is not equivalent to a private model on a laptop if other accounts, administrators, or network clients can reach its files or API.
What network activity remains when inference is local?
Local generation can be isolated while surrounding application activity remains online. LM Studio’s privacy policy describes anonymized searches used when finding or downloading models and device or IP information used for update delivery. It also describes optional cloud services that process requests transiently. Ollama distinguishes local content from account and limited usage metadata, and separately distinguishes cloud-hosted requests.
Common residual activities
| Activity | Why it may contact a service | Privacy response |
|---|---|---|
| Model search | Finding available names, versions, or downloads | Search before the sensitive session, then work offline or block the application’s network access. |
| Model and runtime downloads | Retrieving weights, updates, or dependencies | Download deliberately, record sources, and do not send confidential text during setup. |
| Update delivery | Checking versions and delivering application updates | Schedule updates as a maintenance task and account for device or IP metadata. |
| Optional web search or cloud chat | Augmenting an answer with remote results or hosted generation | Disable the feature for sensitive work and verify the active endpoint. |
| Support and diagnostics | Crash reports, logs, or support emails | Inspect attachments and redact prompts, source code, tokens, and document names. |
Network monitoring should focus on both content and metadata. A firewall can show that a process is contacting a service, but it cannot by itself prove what a plugin does inside an encrypted connection; configuration review and least privilege remain necessary.
Is hosted confidential inference ever compatible with a privacy requirement?
Sometimes, but it is a different choice from local inference. Hosted confidential-inference services run computation on provider infrastructure. OLLM distinguishes hardware-attested trusted execution environment (TEE) processing from policy-based zero data retention (ZDR): a TEE can provide an attestation mechanism for the execution environment, while ZDR is a contractual or policy promise about retention.
Compare the actual execution mode, not just the provider’s privacy headline. A hosted request still leaves your device, and protection depends on the selected mode, provider controls, client security, and key handling. OLLM notes that compromised clients and leaked keys remain outside the protection of its execution guarantees.
| Option | Where inference runs | Primary control | Document boundary | Verification strength | Main residual risk |
|---|---|---|---|---|---|
| Ollama local-only | Your device when the local endpoint is selected | OLLAMA_NO_CLOUD=1 or the server setting |
Local prompts and files remain local under the local-processing policy | Runtime and endpoint configuration | Cloud misrouting or an exposed API |
| LM Studio offline | Your device after model download | Work offline and avoid optional cloud services | Offline RAG documents remain on the machine according to its documentation | Runtime state and offline operation | Download, update, and optional-cloud traffic |
| Hosted confidential inference | Provider infrastructure | Select and verify TEE or ZDR terms | Data leaves the device; protection depends on the selected guarantee | TEE attestation can be technical; ZDR is policy-based | Compromised clients, leaked keys, and incorrect execution-path assumptions |
Choose hosted processing only when its documented controls satisfy your threat model and your organization accepts the provider, jurisdiction, key-management, and incident-response implications.
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How do I verify that a coding assistant is really local?
Check the complete path from editor to model, not only the model’s location. An extension may send file context to its own service, call a remote embedding endpoint, or use web search even when its generation backend is configured to localhost.
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- Open the extension’s provider or base-URL setting and confirm it points to your local server.
- Remove cloud API keys from profiles used for confidential repositories, or use a separate profile with no cloud credentials.
- Review workspace indexing settings and identify where embeddings, caches, and symbol databases are stored.
- Disable telemetry, remote diagnostics, web search, and connector tools where the product allows it.
- Test with a unique canary comment in a non-sensitive repository. Watch network connections while requesting a completion and while indexing files.
- Check editor, runtime, proxy, and operating-system logs for unexpected destinations or uploaded file paths.
Do not paste secrets into a coding prompt merely to test privacy. Use synthetic credentials and a disposable repository, then rotate any token that was accidentally exposed.
What are the most common privacy failures and how do I recover?
The client silently uses a hosted endpoint
Stop the session, revoke any exposed API key, and inspect the client’s base URL and profile selection. Reconfigure it to the local endpoint, disable cloud features at the runtime, restart the service, and repeat the canary test.
A document was sent to web search or a connector
Disconnect the integration, determine which files and fields were transmitted, and treat the incident according to your organization’s disclosure process. Remove connector permissions and rebuild the local index from original files after verifying that the replacement workflow is offline.
The API is reachable from the network
Restore loopback binding, remove broad proxy rules, and rotate credentials if the endpoint had no authentication. Review firewall logs for clients that connected while it was exposed.
Logs or crash reports contain sensitive text
Disable verbose prompt logging, delete local copies according to your retention policy, and redact files before sending diagnostics. Check whether an automatic crash-report service uploaded the material.
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Stop using it for confidential inference. Isolate the machine, preserve evidence if required, rotate credentials from a trusted device, and reinstall or restore from a known-good state before returning sensitive documents.
What should I check before every sensitive research or coding session?
- The model is already downloaded and the runtime starts without an internet connection.
- The active endpoint is local, such as
http://localhost:11434/api, rather than a hosted URL. - Ollama cloud features are disabled with
OLLAMA_NO_CLOUD=1ordisable_ollama_cloud: true, and the service has been restarted. - RAG parsing, embeddings, indexes, and caches are local.
- Web search, remote MCP, cloud fallback, telemetry-heavy plugins, and automatic diagnostics are disabled or understood.
- The API remains bound to
127.0.0.1:11434unless a reviewed, authenticated remote design is required. - Other local accounts cannot read the project directory, model cache, prompt history, or index.
- You know what metadata will be generated when the machine reconnects for updates or support.
Frequently Asked Questions
Can I use a local model safely on a work laptop that is connected to the internet?
Yes, if the runtime, client, and retrieval pipeline are configured for local-only processing and the API stays on loopback. Internet connectivity still permits update, download, telemetry, or connector traffic, so use firewall rules or a planned offline window when the metadata itself is sensitive.
Does deleting a chat erase the documents used by local RAG?
Not necessarily. Original files, extracted text, vector indexes, caches, and logs may be separate artifacts. Locate and remove each copy according to your retention policy, and check backups or synchronization tools that may have copied the project directory.
Can another user on my computer call my local AI server?
A loopback address limits traffic to the machine, not necessarily to one user account. Operating-system permissions, process isolation, and any proxy or port-forwarding rule determine whether another local user can read files or invoke the endpoint.
What is the difference between a TEE guarantee and zero data retention?
A TEE claim concerns where and how computation executes and may be checked through attestation. Zero data retention is a provider policy or contract about keeping requests; it does not by itself prove that the request ran in hardware-isolated infrastructure.
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