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To keep local AI prompts and files private, verify that your chosen runtime performs inference on your PC, turn off any cloud features you do not need, and secure the places where prompts, documents, models, and chat histories are stored. “Local” limits one important risk—sending inference content to a provider—but it does not automatically prevent an app extension, sync service, backup, diagnostic report, or other person with access to the computer from exposing that content.
This guide focuses on personal Windows and Mac PCs and common local runtimes. It explains how to check data paths and reduce accidental exposure without treating a local setup as a guarantee of privacy or security.
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What does “local AI” protect—and what does it not?
A local AI runtime processes prompts and generates responses on your computer rather than sending that inference content to a remote model service. That can reduce third-party access to the conversation. The Office of the Australian Information Commissioner (OAIC) says local or on-premises deployment is likely to be more privacy-preserving because it limits third-party access, while also recommending due diligence on security, access, hosting, and disclosure.
The word “local” describes a data path, not every feature in an application. A program may use a local model for inference while separately connecting to the internet for model downloads, catalog updates, cloud models, web search, extensions, or diagnostics. Those activities are not proof that prompts are being uploaded, but they are separate network paths worth checking.
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- Inference: the prompt and response exchanged with the model. Confirm whether both remain on the PC.
- Files: source documents you select or import. Check whether the app stores copies, creates an index, or saves extracted text, and where it places those items.
- App data: chat history, settings, logs, and crash reports. They may be stored separately from the original documents and model.
- Other services: cloud models, web search, connectors, backups, sync clients, and diagnostics. Assess each independently rather than assuming the inference setting controls them all.
These distinctions matter because a prompt can stay on-device during inference yet later appear in a synced chat-history folder or in a log someone shares for troubleshooting. For confidential or regulated work, follow the applicable organizational policy and legal review; a local runtime alone does not establish compliance.
How can I verify that prompts and responses stay on my PC?
Start with the runtime’s own privacy documentation and identify which features are enabled. A documented local-inference guarantee is useful evidence about that runtime, but it does not certify third-party extensions, operating-system settings, or your backup configuration.
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Ollama’s privacy policy states: “Ollama runs on your local device. We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” Its FAQ separately says that when Ollama runs locally, data is not sent to the cloud. These statements concern local use; they should not be read as a guarantee about every optional feature, connector, or other application on the PC.
Microsoft’s Windows AI FAQ says Foundry Local runs entirely on-device and that inference input and output never leave the machine. It also distinguishes inference from model downloads and optional catalog refreshes, which involve network activity. That distinction is a useful one to look for in any runtime’s documentation: what stays local during inference, and what still communicates with a service?
Use a short verification checklist
- Identify the exact runtime and mode. Confirm that you are using its local model or local inference feature, not a similarly named cloud option.
- Read the privacy and feature documentation. Look specifically for statements about prompts, responses, imported content, chat history, model downloads, web search, and diagnostics.
- List enabled connections. Review cloud features, connectors, extensions, search, and any account-linked services. Turn off what is unnecessary.
- Check storage locations. Find out where the app keeps its models, workspace, imported files, chat history, and logs. These may not all be in one directory.
- Review the setup after changes. Restart the runtime when a cloud or network setting requires it, then confirm that the setting remains in effect.
Documentation can tell you what a vendor says a feature does; it cannot establish that your particular PC is configured correctly. Treat your runtime’s configuration, operating-system accounts, sync choices, and enabled integrations as part of the privacy setup.
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How do I disable cloud features in Ollama?
Ollama documents a local-only mode using either the disable_ollama_cloud setting in ~/.ollama/server.json or the OLLAMA_NO_CLOUD=1 environment variable. This disables access to cloud models and web search, so use it only if you are willing to give up those features.
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- Apply the setting using Ollama’s current configuration instructions. The file path above is written in Unix-style home-directory notation; the correct location and method can depend on how the runtime is installed and launched.
- Restart Ollama. A running server may not pick up a changed setting until it restarts.
- Check that cloud-dependent features are unavailable. Confirm the cloud models and web search you disabled cannot be used in the running setup.
- Recheck after an update or launch change. Make sure the setting is still present and applied if you change how Ollama starts or update the application.
Local-only mode reduces the chance of accidentally choosing those Ollama cloud features; it does not encrypt stored files, prevent another program from accessing them, or control unrelated network activity on the PC. If a cloud feature is needed for a particular task, make that a deliberate exception and avoid placing sensitive content in that workflow.
Where can prompts, documents, models, and chat logs be exposed?
Protect each copy of the data, not just the original document. An AI app may keep conversation history, imported material, or diagnostic information apart from the folders where you expect to find the source files. The exact locations and retention behavior depend on the application and configuration, so inspect the runtime’s settings and storage documentation rather than assuming a single default folder.
Sync folders and backups
A consumer sync client can copy a local workspace to an online account or another device. Keep sensitive AI work out of consumer-sync locations unless that destination and its access controls are approved for the data. Check backup settings as well: determine which folders are included, who can access the backup, and how long copies are retained. Removing a file from the working folder may not remove copies already held in a backup or synced location.
Logs and diagnostic reports
Logs and crash reports can include more than an error message. Before sharing one, search it for prompt text, document excerpts, file paths, account details, and keys or other credentials. Share only what is needed, and remove sensitive material where possible. Do not assume that a report is safe to publish simply because it was generated to troubleshoot an application.
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Extensions and connectors
Extensions and connectors can add useful functions but may also have their own permissions, update behavior, and network access. Remove integrations you do not need. For those you keep, check what data they can read, whether they communicate externally, and whether their behavior changes after updates. A local model does not make an extension’s data handling local.
How should I secure the PC and AI workspace?
Use multiple layers: encrypt the computer, restrict who can sign in and read the workspace, and reduce the chance that an unattended session exposes an open chat. These protections help if a device is lost, shared, or accessed through another account; they do not substitute for checking the runtime’s data flow.
- Enable full-disk encryption. Use the encryption capability available for your Windows or Mac PC and protect access to the computer. Encryption is most useful when the device is powered off or otherwise protected; an unlocked session can still access files available to that account.
- Use a strong login password and automatic screen lock. A strong sign-in helps restrict access, while automatic locking reduces the time an unattended computer remains open.
- Use a separate daily account without administrator rights. Avoid doing ordinary AI work from an administrator account. Limiting routine privileges can reduce what a process running under your account is able to change, though it does not prevent all data access.
- Limit directory access. Keep prompts, imported documents, model files, and chat histories in directories readable only by the accounts that need them. If other people use the PC, check that they do not have access through shared accounts or shared folders.
- Keep sensitive workspaces separate. Use a dedicated location for AI work so it is easier to review access, sync, and backup choices without changing unrelated personal files.
Encryption and access controls protect data at rest and against some forms of unauthorized access. They do not hide data from software that is already authorized to read it while you are signed in. For that reason, account hygiene, app permissions, and the runtime’s behavior all remain relevant.
Should I block the AI app’s network access?
A host-firewall rule can restrict unnecessary outbound connections where your workflow permits, but a blanket block may also stop model downloads, catalog refreshes, updates, or other features you rely on. Treat firewall controls as an additional layer, not as proof that a runtime is private.
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Before changing rules, decide which network activity you actually need. If the model is already installed and the workflow does not require cloud features or web search, blocking unneeded outbound traffic may be appropriate. If you depend on downloads or updates, plan how those will happen and verify the app still works as intended. The precise firewall steps differ by operating system and configuration, so use the firewall’s controls for the PC you have rather than applying a generic rule that could disrupt unrelated traffic.
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Review permissions and network access for extensions separately where possible. A rule affecting the main application may not cover a separate helper process or another program that handles files. If an app’s network behavior is unclear, do not put sensitive material into it until you can establish what the feature does.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I compare local AI options for privacy?
Compare specific features and data paths, not just whether an app calls itself local. Use the same questions for every runtime, and treat an unstated behavior as something to verify rather than as a privacy guarantee.
| What to compare | What to establish | Why it matters |
|---|---|---|
| Cloud controls | Whether cloud models, web search, or other cloud features can be disabled, and how to confirm the setting took effect. | A local model can coexist with optional cloud features. |
| Inference data path | Whether prompts and outputs remain on-device during inference, according to the runtime’s documentation. | This is the central claim behind local inference. |
| Storage locations | Where model files, prompts, imported documents, chat histories, and logs are stored. | Each may be subject to different account access, sync, and backup settings. |
| Extensions and connectors | What each can access, whether it makes network connections, and how updates affect it. | Integrations can create data paths that are separate from inference. |
| Encryption and accounts | How the runtime fits with disk encryption, operating-system accounts, and file permissions. | Local storage still needs protection from other users and unauthorized access. |
| Backups and sync | Which workspace folders are copied, where copies go, who can access them, and how long they are retained. | A local working copy can have remote or shared copies elsewhere. |
For example, Ollama’s published privacy statements address locally processed prompts and responses, while its documented local-only control also removes access to cloud models and web search. Microsoft’s Windows AI FAQ makes a different but complementary distinction: Foundry Local inference stays on-device, while model downloads and optional catalog refreshes are network activity. Neither statement by itself answers where your files are backed up or what a third-party extension can access.
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What should I do before using confidential documents?
Pause before importing confidential, personal, regulated, or employer-owned material. Confirm that both the runtime and the way you manage the PC are allowed for that data; local inference alone does not prove that a workflow meets a legal or workplace requirement.
- Confirm the organization permits the runtime, model, extensions, and data type.
- Check whether local use is genuinely local for the feature you plan to use, and disable optional cloud functions if required.
- Verify workspace permissions, disk encryption, sync exclusions, and backup destinations.
- Limit access to the files and accounts needed for the task.
- Review any logs or reports before sharing them outside the approved group.
If policy or legal requirements are unclear, do not use a personal local setup as a workaround. Ask the organization’s privacy, security, or legal contact which tools and storage locations are approved.
Frequently Asked Questions
Does local AI need an internet connection?
Not necessarily for inference once a model is available on the PC, but related features may use the network. Model downloads, catalog refreshes, updates, cloud models, and web search can involve separate activity; check which ones your chosen workflow requires.
Can a local AI app see every file on my computer?
Do not assume either that it can read everything or that it can access only one selected file. Access depends on the app, operating-system permissions, account, and any extensions or connectors; keep sensitive files in locations unavailable to accounts or tools that do not need them.
Are local AI chat histories automatically encrypted?
The cited runtime statements establish that local inference content stays on-device, not that every stored chat history is separately encrypted. Protect the computer and restrict access to the directories holding app data, and check the specific app’s storage documentation.
Is a local AI setup suitable for employer-confidential data?
Only if the employer’s policies and applicable legal requirements allow that runtime, model, and storage arrangement. Local processing can limit third-party access, but it does not by itself establish organizational approval or compliance.
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