GitHub Copilot Training Data Policy: What Changed in April 2026 and How to Opt Out

On April 24, 2026, GitHub changed its data policy for GitHub Copilot: unless you explicitly opt out, GitHub will use your Copilot inputs (prompts), outputs (completions), and code snippets from interactions to train and improve their AI models. The change applies to individual Copilot subscribers and to Copilot Business and Enterprise seats unless organization administrators disable data collection at the org level.

This is not a breach or a scandal — Microsoft and OpenAI have used interaction data for model improvement throughout the history of most AI products. But for developers working with proprietary code, internal business logic, or client systems, the policy change deserves more than a footnote in a settings changelog. It is a data governance question, and the right answer depends on what you and your organization are building with Copilot.

What data the policy covers

GitHub's policy update specifically covers three categories of data from Copilot sessions:

• Inputs. The prompts you type in Copilot Chat, the inline completion context passed from your editor, and the natural language instructions you give to Copilot Workspace or agent-mode tasks.
• Outputs. The completions, code suggestions, chat responses, and generated files that Copilot returns to you.
• Code snippets. The code context sent with your prompts — typically the current file or selection — and code included in agent-mode task context.

The policy does not cover code that is simply present in your repository but never sent to a Copilot session. Copilot does not crawl your codebase in the background and send it to training. Only what passes through an active Copilot interaction is in scope.

For most routine Copilot usage — autocomplete in a side project, chat about a public library, inline suggestions on open-source code — this policy change has limited practical consequence. The data involved is largely context that could be reconstructed from public sources anyway.

The concern is real for specific workloads:

• Proprietary algorithms or business logic that your organization considers trade secrets
• Client code that your employment or service contract restricts you from sharing with third parties
• Security-sensitive code — authentication flows, cryptographic implementations, authorization logic
• Regulated data in context — any Copilot session where the prompt or code snippet includes PII, financial data, or healthcare information
• Internal infrastructure details — service architecture, credentials handling, internal API contracts

The risk model is not that GitHub is deliberately mining your code for competitive intelligence. The risk model is that training data ends up influencing model outputs in ways that could surface your proprietary approaches in other users' completions — a theoretical risk that is hard to quantify but not zero. For enterprise teams with real IP exposure, that theoretical risk is the kind that legal and security teams want documented and addressed before an incident creates it.

How to opt out

GitHub provides opt-out controls at both the individual and organization level. The process as of July 2026:

Individual opt-out (GitHub.com personal account):

1. Go to github.com/settings/copilot
2. Scroll to the "Allow GitHub to use my data to improve GitHub Copilot" section
3. Toggle the setting to disabled

This applies to your personal Copilot subscription. It does not override organization-level settings if you are also using a Copilot Business or Enterprise seat assigned by your employer.

Organization opt-out (Copilot Business and Enterprise):

1. Go to your organization settings: github.com/organizations/YOUR_ORG/settings/copilot/policies
2. Find the "Allow GitHub to use data from Copilot interactions to improve the model" policy
3. Set to "Disabled" for all members of the organization

Organization admins should note that setting this policy at the org level overrides individual member settings. If you disable collection at the org level, members cannot opt back in from their personal settings. That is the correct configuration for enterprise teams with code governance requirements — it prevents individual developers from inadvertently re-enabling training data collection.

GitHub Enterprise Server deployments: If you are running GitHub Enterprise Server in your own infrastructure, your Copilot interactions are not routed through GitHub's cloud training pipeline in the same way. Check your specific version's data handling documentation and your enterprise agreement with GitHub/Microsoft to confirm the data boundaries that apply to your deployment.

What the policy change does not mean

A few things that are sometimes mischaracterized in discussions about this change:

GitHub cannot see your code through passive repo indexing. Copilot is not watching your private repositories and sending contents to training. The scope is specifically interaction data — prompts, completions, and code context from active Copilot sessions. Repositories that you never use in a Copilot session are not covered by this policy.

Opting out does not degrade Copilot quality for you. Your personal opt-out removes your data from future training runs. It does not affect the model you are using today, and GitHub has not indicated that opting out affects the quality of suggestions you receive.

This is not unique to GitHub. OpenAI, Anthropic, and most AI providers have policies that allow interaction data to be used for improvement unless you opt out or have an enterprise contract with explicit data exclusion. GitHub's April 2026 change aligned with what other providers have been doing, rather than breaking new ground. The difference is that Copilot is integrated into the tools developers use for professional work in ways that chat AI products typically are not — which makes the default opt-in more significant for real codebases.

What enterprise teams should do now

If you are an engineering manager, platform engineer, or security engineer at a company using GitHub Copilot, the practical steps:

1. Check your current org policy setting. If it was never explicitly set, it defaults to enabled. Verify the current state at your organization's Copilot policies page before assuming opt-out is in effect.
2. Classify your code exposure. Identify which repositories contain proprietary business logic, client code under NDA, or regulated data. These are the repositories where developers should either not use Copilot Chat/agent-mode on sensitive files, or where org-level opt-out is non-negotiable.
3. Update your AI tool use policy. If your organization has a policy governing AI tool use (and if it does not, it should), update it to address Copilot training data. Specifically: document what categories of code should not be sent to Copilot at all, regardless of the training opt-out status.
4. Confirm enterprise contract terms. Copilot Enterprise agreements often include data processing terms that supersede the default policy. Check your agreement — if you are on Enterprise and have a data processing addendum, the training data policy may already be addressed contractually.
5. Document the decision. Whether you opt in or opt out, document why. In 12 months when a new developer or a security audit asks about Copilot data handling, you want a clear record of the decision and the reasoning, not an unclear default.

How this affects the Cursor vs Copilot calculus

For developers actively evaluating Cursor versus Copilot, this policy change is relevant but not decisive on its own. Cursor also uses interaction data for product improvement; the specific terms and opt-out controls differ. What the Copilot change does is add data governance as an explicit factor in the comparison, particularly for enterprise teams.

The relevant comparison is not "which tool has a better privacy policy" — both have policies that allow data use with opt-out controls. The relevant comparison is "which tool's data controls and enterprise contract terms map better to your organization's code governance requirements?" For many enterprise teams, Copilot Enterprise's contract terms and the ability to set org-level policies is actually a stronger governance story than what smaller vendors currently offer. But only if someone actually sets the org policy — which this change makes more urgent to do explicitly.

The broader pattern: AI tool governance is now a real job

The April 2026 Copilot policy change is one data point in a larger shift. As AI coding tools move from experiments to standard engineering infrastructure, the governance questions — data handling, credential access, audit logging, policy controls — are becoming first-class concerns rather than afterthoughts. Security teams that ignored these questions while AI tools were novelties now need to answer them as the tools become part of everyday engineering workflow.

The right posture is not panic about AI tools collecting data. Most of what Copilot processes is legitimately low-risk for most developers. The right posture is treating AI tool data policies the same way you treat any other vendor data agreement: read it, classify your exposure, make a documented decision, and enforce it consistently across the team.

Sources: r/github thread: GitHub training data policy change (April 2026), GitHub Docs: Managing Copilot policies for your organization, botspot.dev: GitHub Copilot usage-based billing 2026, botspot.dev: Cursor vs GitHub Copilot in 2026, botspot.dev: AI-generated code security risks.