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There is no universal “do not train” switch. To protect a game project, identify the exact assistant, account tier, IDE or CLI, and model provider that can see the repository; then disable every available training, model-improvement, content-sharing, and telemetry control. Prefer a plan with a documented no-training commitment, keep secrets and unreleased assets out of prompts, and verify the settings again after upgrades or account changes.
GitHub Copilot individual users must disable Allow GitHub to use my data for AI model training. Copilot Business and Enterprise data is excluded from model training. Cursor says Privacy Mode keeps code from training by Cursor and other model providers. Amazon Q Developer Pro excludes content from service improvement and training, while Free Tier content may be used unless the user opts out. Consumer Claude accounts, including Claude Code use, may use sessions to improve models unless Incognito chats are used.
What should you check before opening a game repository?
Start with an inventory, because privacy rules follow the account and deployment that processes a request, not simply the editor on your screen.
- Name the exact product and access method. Record the assistant, IDE extension, command-line tool, hosted integration, and model provider. A team may have different policies for an editor extension and a separate CLI logged into another account.
- Identify the account tier. Individual, team, enterprise, free, and paid plans can have different training terms. Do not infer the policy for one tier from another.
- Find all privacy controls. Look for labels such as training, model improvement, content sharing, data use, or telemetry. A control named “privacy” may cover only one of these.
- Limit the workspace. Open only the repository or folders needed for the task. Exclude build output, asset archives, design documents, credentials, and unrelated projects.
- Remove high-value secrets. Never put API keys, signing certificates, passwords, unreleased assets, proprietary algorithms, or confidential design documents in prompts or files exposed to the assistant.
- Document the decision. For a team, record the approved product and plan, privacy mode, retention terms, telemetry state, repository scope, and an incident-response contact.
Repeat this inventory after an account migration, extension reinstall, model-provider change, or material policy update.
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Does opting out of training mean the code never leaves your computer?
No. A training opt-out addresses how a provider may use data after it is received; it does not establish that the assistant is local-only or offline.
A hosted coding assistant may transmit prompts, selected code, surrounding context, repository metadata, and operational signals so it can generate a response. Depending on the feature and access method, GitHub says Copilot processes prompts, outputs, code snippets, and associated context. A provider may also route requests to subprocessors or selected model providers under its terms.
Evaluate these questions separately:
- Training: Are prompts, outputs, snippets, or context used to train or improve models?
- Retention: How long is content kept, and for what operational or safety purpose?
- Routing: Are requests sent to subprocessors or another model provider?
- Telemetry: Does diagnostics data contain code, or only usage events?
- Administration: Can an organization enforce and audit the setting centrally?
- Scope: Does the same rule cover this plan, IDE, CLI, API, and hosted integration?
If your requirement is that source code remain inside infrastructure you control, choose a deployment whose current technical documentation proves that architecture. Do not call a service “local” or “offline” merely because it offers a training opt-out.
How do I stop GitHub Copilot from using my code for training?
On Copilot Free, Pro, and Pro+, disable the personal setting that permits training. GitHub announced on March 25, 2026 that, beginning April 24, 2026, interaction data from those individual plans would be used to train and improve AI models unless the user opted out.
- Sign in to the GitHub account that your IDE or CLI uses for Copilot.
- Open your GitHub Copilot settings.
- Find the Privacy section.
- Set Allow GitHub to use my data for AI model training to Disabled.
- Save the change, then confirm that the setting remains disabled when you reopen the page.
GitHub describes the affected interaction data as including inputs, outputs, code snippets, and associated context. The setting is for the personal account, so changing a different GitHub account will not protect a session logged in elsewhere.
What changes for Copilot Business and Enterprise?
GitHub says Copilot Business and Enterprise customer data is not used to train AI models. The individual training control is not shown for those plans. Confirm that the repository is actually being accessed through the organization’s Business or Enterprise entitlement rather than a separately logged-in personal subscription.
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What Copilot’s control does not prove
Disabling model training does not prove that prompts or code stay on the computer. Copilot still has to process the context required for its feature. Keep the repository scope narrow and review the organization’s retention, subprocessors, and administrative controls separately.
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Enable Cursor’s Privacy Mode for the account and workspace that can access the game. Cursor’s privacy documentation states: “Privacy Mode ensures your code is never used for training by Cursor or other AI model providers.”
- Open Cursor’s account or privacy controls.
- Turn on Privacy Mode.
- Verify that the project is opened under the same account and team policy.
- Check the current privacy documentation for retention, subprocessors, logging, and model-provider routing.
- Recheck the mode after changing accounts, organizations, models, or installations.
The training guarantee answers one question: whether code is used for training by Cursor or another provider. It does not automatically answer how long operational data is retained, which subprocessors receive it, or what an administrator can audit. Treat those as separate due-diligence items.
Can Claude Code use a private game repository to improve models?
For consumer Claude Free, Pro, Max, and other consumer accounts using Claude Code, Anthropic’s consumer privacy documentation says chat and coding-session data may be used to improve models under the consumer policy.
What does Incognito do?
Anthropic says Incognito chats are not used to improve Claude even when the Model Improvement setting is enabled. Use Incognito for a session only when its behavior, retention terms, and account context meet your project’s policy; do not assume that an Incognito conversation changes the rules for other sessions or integrations.
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The consumer statement does not automatically apply to an API account, enterprise agreement, hosted platform, or third-party deployment. Check the terms governing the exact deployment before sending proprietary code. A developer who uses Claude Code through a different organization or service may be subject to that service’s policy rather than Anthropic’s consumer policy.
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Is Amazon Q Developer safe for a confidential project?
Amazon Q Developer has a clear tier distinction: AWS says Pro content is not used for service improvement or model training, while Free Tier content may be used for service improvement, including model training.
Amazon Q Developer Pro
For Pro users, AWS states: “Your code content is not used for service improvement or model training.” Confirm that the IDE or command-line session is authenticated to the Pro subscription and not silently falling back to a Free Tier identity.
Amazon Q Developer Free Tier
Free Tier content may be used for service improvement. AWS documents opt-out controls through IDE settings or organization-level policies, depending on the environment. In Visual Studio Code, the documented path is the Amazon Q settings, followed by disabling Share Amazon Q content with AWS.
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AWS treats content sharing and client-side telemetry as different controls. Telemetry can include usage signals such as whether recommendations were accepted or rejected; AWS’s IDE opt-out documentation says that this telemetry does not contain actual code. Disabling content sharing therefore does not necessarily disable every diagnostic event, and disabling telemetry does not by itself establish a no-training commitment.
Which privacy control is strongest for an indie game?
The strongest choice is a plan-level or contractual no-training commitment that covers the exact account and deployment, backed by administrative enforcement. An opt-out is still useful, but it relies on a user or administrator keeping the setting enabled.
| Assistant or account | Documented training treatment | Primary control | Important scope check |
|---|---|---|---|
| GitHub Copilot Free, Pro, Pro+ | Interaction data may be used for training and improvement from April 24, 2026 unless the user opts out. | Disable Allow GitHub to use my data for AI model training in Copilot Privacy settings. | The setting belongs to the personal account used by the IDE or CLI. |
| GitHub Copilot Business and Enterprise | GitHub says customer data is not used to train AI models. | Use the organization entitlement and its administrative controls. | Ensure the session is not authenticated to a personal plan. |
| Cursor with Privacy Mode | Cursor says code is never used for training by Cursor or other AI model providers. | Enable Privacy Mode. | Review retention, subprocessors, logging, and provider routing separately. |
| Claude consumer accounts and Claude Code | Chats and coding sessions may be used to improve models under the consumer policy. | Use Incognito chats, which Anthropic says are not used to improve Claude even when Model Improvement is enabled. | Do not generalize the consumer policy to APIs, enterprise plans, or third-party deployments. |
| Amazon Q Developer Pro | Content is not used for service improvement or model training. | Authenticate to the Pro tier. | Verify the active identity in each IDE or CLI. |
| Amazon Q Developer Free Tier | Content may be used for service improvement, including model training. | Use the documented IDE or organization opt-out; in VS Code, disable Share Amazon Q content with AWS. | Content sharing and client-side telemetry are separate settings. |
For commercially sensitive code, the practical ranking is: a documented enterprise or Pro exclusion first; a product-level privacy mode with clear provider coverage next; and an individual opt-out only when no stronger plan is available and the setting can be verified.
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How should a team protect an unreleased game?
Write a short policy that turns privacy choices into repeatable engineering practice rather than relying on each developer’s memory.
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- Define repository boundaries. Specify which repositories and folders may be indexed or attached to prompts. Keep production credentials, signing material, unreleased levels, and confidential design files outside that scope.
- Set privacy requirements. Require a documented no-training commitment or an enabled opt-out, plus a separate review of retention, subprocessors, and telemetry.
- Centralize enforcement where possible. Use organization policies and managed accounts instead of personal subscriptions for team repositories.
- Record verification evidence. Keep the date, account, plan, privacy setting, and policy version in the team’s security record.
- Define an incident path. State whom to contact if code is pasted into the wrong assistant, a setting changes, or a new provider is enabled.
Recheck the policy whenever a vendor changes terms, a new model provider is enabled, the team migrates IDEs, or someone changes from an individual account to a team or enterprise account.
How can I reduce the amount of game code an assistant sees?
Even with a no-training setting, minimizing context reduces exposure and limits the damage from an account or configuration mistake.
- Open a dedicated workspace containing only the files needed for the current task.
- Exclude environment files, credential stores, certificate directories, build artifacts, crash dumps, and player data.
- Do not ask the assistant to scan the entire monorepo when a single module or test fixture is sufficient.
- Replace proprietary names, keys, and endpoints with neutral placeholders before requesting an explanation.
- Keep unreleased art, audio, narrative documents, and business plans outside the indexed workspace unless the task genuinely requires them.
- Review generated prompts and context attachments before sending them, especially when using automatic “chat with project” features.
These controls reduce transmission; they do not replace the provider’s training and retention policy.
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Verification should be a short repeatable check performed after every material account or software change.
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- Confirm the signed-in account in the IDE, CLI, and provider dashboard.
- Confirm the active plan or organization entitlement.
- Open the provider’s privacy page and check the training or model-improvement control.
- Check content-sharing and telemetry settings independently.
- Review the selected model provider and any routing or subprocessors disclosed for that feature.
- Inspect workspace exclusions and ensure secrets are not included in indexed folders.
- Record the date and result; repeat after extension updates, reinstallations, account switches, or policy changes.
If a setting is missing, unexpectedly re-enabled, or controlled by another organization, stop sending proprietary code until the account owner or administrator confirms the applicable terms.
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What should I do if a setting is unclear?
Do not infer protection from a vague label. Pause the integration and ask the provider or administrator a narrowly defined question covering training, retention, routing, telemetry, and the exact plan.
- “Privacy” without a definition: Find out whether it means no training, no retention, no logging, or merely encrypted transport.
- Different behavior in different editors: Compare the account and deployment behind each extension or CLI; they may not share settings.
- Free and paid tiers mixed: Treat the least-protected active tier as the risk until every session is moved to the approved plan.
- New model provider: Recheck whether the no-training promise covers that provider, not only the assistant’s own models.
- Accidental disclosure: Revoke exposed credentials immediately, remove the affected access, document what was sent, and follow the team’s incident process.
Frequently Asked Questions
Can a private Git repository still be sent to an AI assistant when training is disabled?
Yes. Training controls govern later use of received data, while hosted features may still transmit prompts, code context, repository metadata, or operational signals. Check transmission, retention, and provider routing separately.
Does an enterprise plan automatically cover a developer’s personal IDE login?
No. The protection follows the account and entitlement actually authenticating the request. Verify that the IDE or CLI is signed in to the organization-managed plan rather than a personal subscription.
What is the difference between code telemetry and code content sharing?
Content sharing concerns material sent for an assistant’s service or improvement workflow. Telemetry can be limited to usage events, such as whether a suggestion was accepted; AWS documents that its client-side telemetry does not contain actual code, but the two settings remain separate.
Should I recheck AI privacy settings after an extension update?
Yes. Reinstallation, account migration, model changes, and new provider integrations can alter which account or policy applies. Keep a dated verification record and repeat the check after each material change.
