What Exactly Did GitHub Change?
Instead of relying on summaries, I went straight to GitHub's own wording, because headlines like "GitHub will train on your private repos" sound more alarming than the reality. The fact is, GitHub isn't about to start scanning your private repositories wholesale and adding them to a dataset.
The change concerns interaction data in Copilot. This includes what's generated during your workflow: prompts, suggestions, code snippets, and the context Copilot receives in a session. If you're a Copilot Free, Pro, or Pro+ user, this data may be used to improve the models unless you explicitly opt out before April 24, 2026.
And here's the crucial distinction. GitHub does not claim to use private repo code at rest—the actual contents of your private repository—as a training source. This is significantly milder than the scary thesis from discussions on HN.
Another nuance: this doesn't apply to Copilot Business and Enterprise. Corporate plans have different rules, and this update primarily impacts individual accounts. If your team uses personal Pro accounts to work with client code, that's a reason to open your settings today.
Based on the current navigation, you need to check your Copilot features settings. The link being shared in discussions leads to where you can review this option. I wouldn't rely on GitHub's old promises from 2024: the policy has already changed, and now the details must be read literally.
Why This Isn't a Minor Issue for Businesses
On a personal level, the news might not seem so terrible. So what if it's not the entire repository, just interaction data? But when I look at this as an engineer who designs AI architectures for client systems, the picture quickly becomes less comfortable.
In real-world development, sensitive pieces often pass through Copilot: SQL queries with table names, internal APIs, domain logic, infrastructure code snippets, and comments with business context. Formally, this is "interaction data," but in reality, it could contain half the blueprint of your product.
This is where those with disciplined access controls, plan management, and assistant usage policies have an advantage. The losers are teams where developers spontaneously use personal Copilot accounts on private work repos, while management thinks, "AI automation is just a convenience for our programmers for now."
At Nahornyi AI Lab, I see the same thing over and over: AI implementation fails not because of the models, but because of data governance. People spend weeks choosing an LLM but have no idea what data is actually leaving their perimeter, who enabled it, and on which accounts.
If you're already integrating artificial intelligence into your development workflow, I would check three things. First, which Copilot plans are being used—personal or corporate. Second, whether the opt-out for model training is enabled. Third, what internal rules you have for handling private code, tickets, and infrastructure secrets in AI tools.
And this isn't just about GitHub anymore. This is what mature AI integration looks like: understanding where a model helps, where productivity grows, and where a context leak begins through a convenient interface.
My personal conclusion is this: there's no need to panic, but you shouldn't brush it off either. The statement "it's not that bad" is generally fair, as long as you understand the boundaries. Not your entire private repo is being used for training, but snippets of your work context from Copilot sessions for individual users very well could be.
This analysis was done by me, Vadim Nahornyi of Nahornyi AI Lab. I build bespoke AI solutions for businesses, design AI-powered automation, and usually start not with a fancy demo, but with the question: what data is actually flowing where?
If you'd like, I can help you quickly analyze your case: Copilot, access policies, secure AI implementation, or the architecture of internal AI tools. Get in touch at Nahornyi AI Lab, and we'll look at your project without the marketing fog.