Cursor vs GitHub Copilot: Which Is Safer for Your Codebase? (2026)

Most comparisons of these two tools argue about autocomplete quality. That is not the part that will get you in trouble. The part that matters is what each tool does with your source code when you have not touched a single setting, and that answer changed this year.

The default that changed in April 2026

GitHub announced that starting April 24 2026, interactions with Copilot on the Free, Pro and Pro+ plans may be used to train and improve its AI models. The data in scope is not just the text you type. GitHub's description covers "outputs accepted or modified by the user, inputs sent to GitHub Copilot, including code snippets shown to the model, code context surrounding the user's cursor position."

It is opt-out. The setting is on unless you go and switch it off, under Settings > Copilot > Features, in the privacy section at the bottom.

Copilot Business and Copilot Enterprise are excluded. Those plans are covered by GitHub's Data Protection Agreement, which contractually prevents it.

The developer reaction to the announcement is worth noting: the community discussion where GitHub published the FAQ drew 312 downvote reactions against 4 upvotes. Not a scientific sample, but a clear read on how much of a surprise the change was.

What Cursor does instead

Cursor's control is a single switch called Privacy Mode. With it enabled, Cursor maintains zero data retention agreements with its model providers, so the providers do not store inputs and outputs or train on them. Cursor's docs add that cached file contents are "temporary, never permanently stored, and never used as training data."

With Privacy Mode off, Cursor's data use page is direct about it: the company may "use and store codebase data, prompts, editor actions, code snippets, and other code data and actions to improve our AI features and train our models."

So far that sounds like the same trade as Copilot. The difference is the default, and here the documentation is uneven. Cursor states that Privacy Mode is on by default for Enterprise teams, and enabled by default for all team members on Teams. For an individual Free or Pro account, it does not state a default at all.

One more Cursor detail that rarely makes it into comparisons: some models fall outside Cursor's zero-retention agreements entirely. Those models are off by default and need admin approval before anyone on a team can use them. That is a genuinely good default.

Data handling, corrected

The table below replaces the one this post carried before. Two rows in the old version were wrong, and they were wrong in the direction that flatters Copilot.

Agent permissions: the newer risk

Both tools moved from suggesting code to running it, and that shifted where the real exposure sits. An autocomplete that suggests a bad line is a code review problem. An agent with a terminal is an access-control problem.

Cursor has three Run Modes. Auto-review is the default as of version 3.6. Allowlisted calls run immediately, other shell commands run in a sandbox where feasible, and the rest go to a classifier that can allow, block, or ask you. The sandbox blocks network access by default and opens it through a built-in allowlist of roughly 100 domains covering package managers and language tooling. The third mode, Run Everything, does what the name says: every tool call executes with no intervention.

Cursor's documentation includes a sentence that deserves more attention than it gets. Auto-review, the default, "is not a security boundary."

That is the vendor telling you not to treat it as one. Take them at their word.

GitHub Copilot's coding agent takes a different shape. It works in an ephemeral environment powered by GitHub Actions, opens exactly one pull request per task it is assigned, and works on one branch at a time. By default it can only access context in the repository you specified when you started the task.

The PR-shaped output is the underrated safety property here. Everything the agent did arrives as a diff somebody has to approve, rather than as changes already sitting in your working tree.

Both still generate insecure code

Nothing above addresses the vulnerability that ships to production. Both tools produce the same recurring patterns, and we see these constantly in scans of AI-built apps:

• Hardcoded secrets. API keys pasted inline because the model saw that pattern a million times in public repos.
• String-concatenated SQL instead of parameterized queries.
• Missing output escaping in generated UI code.
• Disabled TLS verification and permissive CORS, added to make something work locally and never reverted.

The model you picked barely moves this. Your prompt and your review do.

Which should you choose?

If you are an individual paying for either one, the honest answer is that the plan tier matters more than the brand. Both vendors give their contracted business customers stronger guarantees than their self-serve users, and they are upfront about it in their own documentation.

Further reading

Made your choice? Here's how to secure the stack you picked.

• Pre-deployment security checklist
• Run your first security scan
• API security best practices