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Change three things separately: whether a provider may use your interactions to improve models, which repositories and files the tool can index or retrieve, and what its agents or connected tools can access. A training opt-out does not turn off indexing, and an ignore rule is not necessarily an agent-wide security boundary. Check your account type, workspace policy, repository, and actual IDE, CLI, or agent before relying on a setting.
Which privacy settings should I review first?
Start by identifying the exact account and workflow you use, then make decisions in this order: training, repository access and indexing, sensitive-file exclusions, and agent or connected-tool permissions. These controls address different data paths; changing one does not automatically change the others.
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- Identify the account and policy. Check whether you use an individual account or an organization-managed seat, and whether the work happens in an IDE, CLI, cloud agent, or another surface. An organization policy may set or limit options that are not available to an individual.
- Decide about model improvement. Review the product’s data-use setting and turn off training or improvement use if you do not consent to it.
- Limit what the tool can search. Review repository access and whether indexing is automatic. Grant access only to the repositories the workflow needs.
- Exclude sensitive paths where supported. Keep credentials out of the working context, then configure the product’s own file-exclusion mechanism.
- Review the rest of the workflow. Check sandboxing, network access, project trust, environment variables, partner agents, web search, and enabled MCP servers or tools, where applicable.
- Verify the actual surface. An exclusion or permission may apply in one product surface but not another. Test in the IDE, CLI, or agent where you intend to work.
This is a settings guide, not a guarantee that a provider never receives code. Local and cloud workflows, indexing, requests, and connected tools can have different data paths, so assess the specific workflow rather than relying on a single privacy label.
Does turning off AI training stop code indexing?
No. Training controls and indexing controls answer different questions. A provider may not use an indexed repository to train a model while still indexing it to retrieve relevant code for an answer.
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GitHub says repository-context Copilot Chat automatically indexes the repository. GitHub also states, “Copilot will not use your indexed repository for model training.” That statement is about training use of the index; it does not mean the repository is not indexed, or that no code is processed to provide repository context.
For non-GitHub workspaces, GitHub says VS Code semantic indexing uploads data to GitHub. For managed Business and Enterprise seats, that indexing policy is disabled unless an organization or enterprise owner explicitly enables it. This distinction matters if a workspace is not hosted on GitHub: switching off model training does not turn off the documented upload associated with semantic indexing.
Before enabling repository context, ask what the tool searches, whether indexing happens automatically, and whether non-hosted workspace contents are uploaded. If you do not need repository-wide context, consider limiting the tool’s repository access or not enabling that workflow.
How do GitHub Copilot training settings differ by plan?
GitHub’s stated policy is plan-specific. Beginning April 24, 2026, interactions on individual Free, Pro, Pro+, and Max plans may be used for training and improvement; GitHub provides an individual setting to opt out. GitHub says Business and Enterprise customer data is not used for that purpose without customer authorization, and the personal setting is not shown for those plans.
On an individual Copilot account, inspect Copilot settings and set Allow GitHub to use my data for AI model training to Disabled if you do not consent to that use. Do not assume you will see this control on a managed plan: GitHub describes a different policy for Business and Enterprise, with authorization by the customer rather than the same personal switch.
If your account is managed, ask the organization or enterprise owner what policy applies and whether the organization has authorized training use. A managed seat’s policy is not necessarily changed by an individual preference, and the setting’s absence does not itself explain the organization’s configuration.
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Does OpenAI train on my Codex usage?
OpenAI says ChatGPT training-data controls apply to Codex content. Its Help Center states, “Your ChatGPT training data controls apply to content processed through Codex, including screenshots taken by Computer Use.”
OpenAI says Pro and Plus conversations may improve models unless training is turned off, while business products have different defaults. Review the applicable ChatGPT training-data control for the account and product you use rather than assuming that a Codex task has a separate opt-out. The plan distinction matters: the stated Pro and Plus behavior should not be generalized to business products.
Codex also has separate execution and access considerations. OpenAI documents that local workflows run on the device, while cloud tasks run in OpenAI-managed environments. Location alone does not establish what data is sent during a request. For sensitive projects, review sandbox mode, network access, project trust, inherited environment variables, and which MCP servers or other tools are enabled.
How can I limit which repositories a Copilot cloud agent can access?
For a personal Copilot account, the cloud-agent Repository access control offers three choices: No repositories, All repositories, or Only selected repositories. If you need the agent for only a few projects, select only those repositories instead of granting access to all.
Also consider whether you need partner agents or web search enabled. If you do not use them, restrict or disable them where the product provides that choice. Repository access is only one part of the permission picture: review what the agent can do within an allowed repository and what connected services it can reach.
For Codex, make the analogous decision through the documented controls for the workflow: sandbox mode, network access, project trust, inherited environment variables, and enabled MCP servers or tools. These are not interchangeable with a model-training opt-out. They govern execution context or access, so review them before starting a task involving sensitive work.
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How do I exclude sensitive files such as .env from Copilot?
First, avoid putting credentials or secrets into the assistant’s working context. Then use the product’s supported exclusion mechanism for private configuration, secret files, and confidential directories. Exclusion support depends on the product, account, and surface; do not treat one tool’s ignore file as a universal protection.
Copilot Business and Enterprise
GitHub documents content exclusion for Copilot Business and Enterprise at repository, organization, and enterprise scopes, with examples that include .env and secret-named files. Organization rules can also address files outside Git repositories. These are administrator or owner controls; do not assume that the same configuration is available to every personal account.
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Devin Desktop
Devin Desktop documents a .devinignore file at the repository root for excluding additional paths from both indexing and agent access, on top of its default ignore rules. It also documents a global .codeiumignore for enterprise users. Those semantics belong to Devin’s documented controls; they should not be assumed to describe GitHub Copilot or another coding tool.
What an exclusion does not establish
GitHub says Copilot CLI and IDE Copilot Chat Agent mode do not support content exclusion. Its documentation also describes limitations involving indirect IDE semantic information, symlinks, and remote filesystems. An exclusion therefore should not be treated as proof that every agent, connected tool, or indirect information path is blocked.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I check whether an exclusion is working?
Do not infer enforcement from a saved rule alone. GitHub says changes to content exclusions can take up to 30 minutes to propagate to IDEs that have already loaded settings, so reload the applicable IDE settings before retesting when appropriate.
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- Confirm the scope and path. Verify the rule is configured at the intended repository, organization, or enterprise scope and names the file or path you mean to exclude.
- Allow for propagation. If an IDE already loaded its settings, account for GitHub’s stated possible delay of up to 30 minutes, then reload the relevant settings or IDE before checking again.
- Compare an ordinary file with an excluded file. GitHub’s documented test procedure compares inline suggestions in a normal file and an excluded file.
- Check chat context separately. GitHub’s procedure also checks whether Chat can use an excluded file when that file is attached as context.
- Repeat in the real workflow. If you use a CLI, Agent mode, remote filesystem, symlink, or connected tool, verify what that surface supports instead of assuming the IDE test covers it.
A successful check in one surface does not prove that another surface enforces the same rule. In particular, GitHub explicitly says Copilot CLI and IDE Copilot Chat Agent mode do not support content exclusion, so do not rely on exclusion to protect files in those workflows.
What does “Suggestions matching public code” control?
Copilot’s Suggestions matching public code choice concerns output: where supported, it controls whether suggestions matching public code are blocked. It is not a setting that opts out of input processing, repository indexing, or agent access.
Treat it as a separate output-policy choice. If your concern is whether the assistant can search a repository, whether files are indexed, or whether interactions may be used for model improvement, review the corresponding controls rather than relying on this setting.
Frequently Asked Questions
How long can Copilot take to index a large repository?
GitHub documentation says initial indexing of a large repository may take up to 60 seconds. That is a possible indexing time, not a privacy metric or a guarantee about the time required for every repository.
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What does Copilot compare when checking for public-code matches?
GitHub says its check compares about 150 characters of surrounding code alongside a suggestion. This concerns whether a suggestion matches public code; it does not describe how repository indexes are stored or used.
Do ChatGPT training controls cover screenshots processed by Codex Computer Use?
Yes. OpenAI’s Help Center explicitly says ChatGPT training-data controls apply to Codex content, including screenshots taken by Computer Use.
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