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Short answer: You cannot use most hosted AI coding features without sending at least some workspace data off the machine. The practical goal is to control exactly what leaves, where it goes, how long it is retained, whether it is used for training, and which agents or extensions can act on it. For code that is not allowed to leave a device, disable cloud AI features and do not sign in to a hosted assistant. For projects that may use cloud AI, reduce context, exclude sensitive paths, turn off telemetry, choose plans with suitable training terms, and audit every extension and provider boundary.
What does an AI coding assistant actually send?
An editor does not usually upload an entire repository for every question. It assembles a request from your prompt and whatever context the feature believes will help: open files, nearby code, recently viewed content, terminal output, diagnostics, symbols, and tool results. That assembled request then travels to the editor’s service, a model provider, or both.
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Cursor documents a similar architecture: AI requests include prompts and code context and are sent through Cursor infrastructure to model providers such as OpenAI, Anthropic, and Google. The important question is therefore not simply whether an assistant is in your editor, but which component receives each piece of context.
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Transmission is only one privacy question
Evaluate these issues separately:
- Transmission: Does any code, prompt, output, or diagnostic leave the workstation?
- Routing: Which company and model provider receive it, and are subprocessors or regional services involved?
- Storage: Are prompts, responses, embeddings, caches, logs, or repository snapshots retained?
- Training: May your content improve a model, or is it excluded by a plan or privacy setting?
- Agent execution: Can a cloud agent clone or retain a repository while it edits, tests, or opens pull requests?
- Telemetry: Does the editor or an extension send usage, crash, command, or file metadata separately from AI requests?
- Deletion: Can you remove stored data, and does deletion apply to copies held by providers or subprocessors?
A “no training” promise answers only the training question. It does not mean that the request stayed on your laptop or that temporary caches and agent workspaces do not exist.
How can I tell what context will be shared before enabling an agent?
Start with an inventory, not a privacy toggle. Identify every source an assistant can read and every action it can perform in the project.
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- Check the assistant’s context rules. Determine whether it can use the active file only, neighboring files, the whole workspace, recent tabs, search results, terminal output, diagnostics, symbols, or previous conversations.
- Inspect tool permissions. An agent that can run shell commands may read files indirectly through command output. A test or build command can print secrets even if the assistant never opened the secret file.
- Review extension permissions and policies. An editor’s built-in setting does not automatically govern a third-party extension. Read the extension’s data-collection and provider documentation separately.
- Test with a harmless repository. Before connecting a sensitive project, use a disposable folder and observe the account, provider, network, logs, and agent workspace described by the product documentation.
Keep a written map containing the feature name, destination service, context types, retention, training treatment, geographic processing, subprocessors, and deletion procedure. Update it whenever an extension, model, or plan changes.
How do I keep a project entirely on the machine?
If policy says source code cannot leave the device, the reliable control is to disable the cloud feature rather than rely on a “private” or “no-training” label. In VS Code, Microsoft documents that disabling built-in AI features, or not signing in to Copilot, prevents data from being sent to Copilot backend services. Third-party extensions can still transmit data under their own policies, so remove or disable them as well.
A practical air-gapped checklist
- Disable built-in chat, inline completion, and agent features.
- Sign out of hosted coding assistants and remove their extensions from the restricted profile.
- Block unapproved extension installation and outbound traffic where your security policy requires it.
- Use a separate editor profile or machine for projects that are allowed to use cloud assistance.
- Check build scripts, language servers, package managers, and crash reporters; an AI assistant is not the only process that can transmit source or diagnostics.
- Review logs and network controls after configuration, then repeat the check after major editor updates.
The documentation covered here does not establish a current, supported matrix of fully offline coding assistants, local models, or hardware requirements. Do not promise an offline deployment until the exact runtime, model, version, and operating conditions have been verified for your environment.
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What does VS Code telemetry off actually do?
Set "telemetry.telemetryLevel": "off" in VS Code settings to disable crash reports, error telemetry, and usage data going forward, according to Microsoft’s documentation. This setting addresses VS Code telemetry; it is not a blanket switch for AI extensions, language servers, package tools, or other software installed in the editor.
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Telemetry opt-out is also not retroactive. Data already sent is not recalled when you change the setting. Treat the change as a forward-looking control, then verify that extensions and connected services have their own opt-out or uninstall path.
Recommended order in a restricted workspace
- Open Settings and search for
telemetry.telemetryLevel. - Set the value to
off, or add the JSON setting"telemetry.telemetryLevel": "off". - Restart VS Code and confirm that no organization policy or profile is overriding the value.
- Audit installed extensions individually; their telemetry is not necessarily governed by VS Code’s setting.
- Disable or remove any AI extension that is not approved for the project.
Does Cursor Privacy Mode keep code on my laptop?
No. Cursor’s Privacy Mode prevents code from being used for training by Cursor or its model providers, but Cursor still sends prompts and code context to those providers when you use AI features. Privacy Mode is a training-use control, not a local-only mode.
How to reduce exposure in Cursor
- Use
.cursorignoreto list files and directories that should be excluded from AI requests. Cursor describes this as a way to block specific files from being sent to its servers; treat it as a best-effort exclusion and verify behavior for the feature and version you use. - Keep secrets outside the workspace where possible. An ignore rule cannot protect a secret that is printed into terminal output, diagnostics, generated logs, or a prompt.
- Review the providers selected for your account because requests may pass through Cursor infrastructure to different model companies.
- Do not enable Cloud Agents when repository storage is prohibited. Cursor says Cloud Agents store encrypted repository copies temporarily while an agent runs.
Cloud-agent storage changes the risk model: even if an interactive completion uses a small code window, an agent may need a repository copy to plan, edit, and test. Ask how that copy is deleted, which providers can access it, and whether the feature is covered by the same privacy mode as ordinary editor requests.
How do Amazon Q Developer plans treat private code?
Amazon documents different treatment for its paid Pro plan and Free Tier. Amazon Q Developer Pro content is not used to train the underlying foundation models. That statement limits training use; it does not make the service an on-device assistant or establish a universal retention period for every integration.
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Amazon also says Free Tier content—including code snippets, conversations, and open-file contents—may be stored and processed for service improvement. If a project cannot be used for that purpose, do not place it in the Free Tier workflow. Select the plan and organization controls that match the project’s contractual requirements, then confirm the current terms for the IDE and command-line integrations you will use.
Telemetry controls for Amazon Q
AWS documents IDE and command-line telemetry opt-out controls. Apply the documented opt-out in each environment rather than assuming that changing an editor setting disables Amazon Q collection. Record the setting in your developer-machine baseline and recheck it after reinstalling the extension or changing accounts.
How do the main controls compare?
The following comparison separates what the product documentation establishes from decisions you still need to verify for a particular account, integration, and version.
| Tool or plan | What can leave the workspace | Training treatment documented | Important controls and limits |
|---|---|---|---|
| VS Code built-in AI with Copilot | File contents, terminal output, diagnostics, and other context supplied to language models and tools when AI is enabled | Depends on the connected service and its terms | Disable built-in AI or do not sign in to Copilot to prevent data being sent to Copilot backend services; VS Code telemetry is separate |
| Cursor | Prompts and code context sent through Cursor infrastructure to model providers such as OpenAI, Anthropic, and Google | Privacy Mode prevents use for training by Cursor or model providers | .cursorignore excludes listed paths on a best-effort basis; Cloud Agents temporarily store encrypted repository copies |
| Amazon Q Developer Pro | Requests required by the hosted IDE or command-line service | Content is not used to train underlying foundation models | Use AWS-documented IDE and command-line telemetry opt-out; confirm retention and provider boundaries for your integration |
| Amazon Q Developer Free Tier | Code snippets, conversations, and open-file contents may be sent to the service | Content may be stored and processed for service improvement | Do not use for code barred from service-improvement processing; apply the documented telemetry opt-out where available |
No row in this table means “nothing leaves the computer.” A hosted feature must receive enough information to produce a result, and the exact request can vary with the prompt, open files, diagnostics, tools, and agent mode.
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How should a team approve an AI coding tool?
Use a written review that treats the editor, extension, model provider, and agent as separate components. A useful approval record contains:
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- Data map: exact context types, file-selection rules, terminal and diagnostic access, and whether prompts are linked to an account.
- Provider map: first-party service, model providers, subprocessors, regions, and cross-border processing.
- Retention map: request logs, response logs, embeddings, caches, backups, and temporary repository copies, with deletion timing and account controls.
- Training statement: whether content is excluded by default, by plan, or only after an opt-out.
- Agent boundary: whether a cloud agent clones the repository, runs commands, stores artifacts, or opens changes.
- Operational controls: disabled-feature procedure, ignore files, telemetry opt-outs, extension allowlists, network restrictions, and offboarding steps.
- Incident path: whom to notify if a secret or regulated file is submitted, and how to revoke credentials and request deletion.
Classify repositories before enabling assistance. A simple policy can allow cloud AI for public code, require explicit approval for internal code, and prohibit it for credentials, regulated records, export-controlled material, or projects with a contractual no-transfer clause.
What should I do if private code or a secret was sent?
Act as though the exposed value is compromised, even if a product says it does not train on the content. Stop the feature, preserve the relevant account and editor details, and follow your organization’s incident process.
- Revoke and rotate exposed API keys, passwords, tokens, signing keys, and certificates.
- Record the assistant, account, plan, model provider, time, repository, files, prompt, and agent mode involved.
- Ask the service for available deletion controls and clarification about provider and subprocessor copies.
- Review repository history, terminal logs, CI output, and agent artifacts for additional exposure.
- Remove the sensitive material from the workspace and prevent it from reappearing in generated diagnostics or logs.
- Update ignore rules, extension policies, and user training before re-enabling any assistant.
Do not rely on deleting a chat window as proof that every copy has disappeared. Retention, backups, caches, and provider-side logs may follow separate procedures.
Can privacy settings make a cloud assistant safe for every project?
No single setting provides universal protection. Privacy Mode, a no-training term, telemetry opt-out, and an ignore file each address a different pathway, while agent execution and third-party extensions can create additional pathways.
For a project that must remain local, disable cloud AI and remove unapproved extensions. For an approved cloud workflow, minimize context, exclude sensitive paths, avoid printing secrets, choose a plan with suitable training terms, disable unrelated telemetry, prohibit cloud agents when repository copies are unacceptable, and document the remaining risk.
Frequently Asked Questions
Does deleting an AI chat delete the code the provider received?
Not necessarily. Chat deletion may cover the visible conversation while request logs, caches, backups, embeddings, or provider and subprocessor records follow separate retention rules. Check the service’s deletion documentation and request process for the exact feature and account.
Can an ignore file protect secrets printed by a build or test command?
No. Ignore rules target file paths, while terminal output and diagnostics can become model context independently. Prevent commands from printing secrets and use redacted test fixtures in addition to path exclusions.
Is a paid AI coding plan automatically private?
No. Payment and privacy are separate properties. A paid plan may change training treatment or administrative controls, but hosted requests can still leave the machine and may still be retained or routed to providers.
Should I disable telemetry if I already disabled AI features?
Yes, if your policy limits diagnostic or usage data as well as source code. Editor telemetry and AI requests are separate channels, and extensions may collect their own data even when built-in AI is off.
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