Last updated: September 1, 2026 · By Vishal Swami, Founder & Lead AI Reviewer, AISagely
AI-written code is still your code the moment you accept the suggestion and commit it — legally, security-wise, and in every code review that follows. The tool that generated the line carries none of the liability; you or your employer do, whether GitHub Copilot, Cursor, or Claude wrote it.
Short answer: Accepting an AI suggestion makes it yours for copyright, security, and review purposes — no different from code you typed yourself. GitHub's IP indemnity for Copilot Business and Enterprise covers unmodified suggestions when filtering is on, but liability for bugs, vulnerabilities, and license violations always lands on the developer and company who shipped it.
I've spent the past few months running the same check on every pull request an AI assistant touches in my own repos: before merging, I ask who's accountable if this breaks. In my testing across GitHub Copilot, Cursor, and Claude Code, the answer never moves — the vendor's terms put the burden back on me, and my code review process treats an AI-authored diff exactly like a human-authored one, mistakes included. That's not a technicality buried in a EULA nobody opens. It shows up in copyright filings, security audits, and the awkward postmortem when a shipped bug traces back to a suggestion nobody actually read. This guide covers what "still your code" means in practice, with the real numbers behind why it matters.
What you'll need
Nothing exotic — just the review habits you'd already want for human-written code, applied without exceptions. That means a code review process that doesn't wave AI-authored diffs through faster than human ones, a static analysis or dependency scanner you already trust, and a way to check for license or duplication risk (GitHub's Copilot has a built-in duplication detection filter; other tools require a separate scanner). You'll also want to know which plan you're actually on, since indemnity terms differ by tier, covered in the table below. None of this requires new tooling if you already run a real review process — it requires not skipping it because the code "wasn't really written by anyone."
Step-by-step: taking ownership of AI-written code
1. Read every line before you accept it
This sounds obvious and gets skipped constantly. Treat an accepted suggestion the same way you'd treat a stranger's patch landing in your inbox — read it fully before it becomes part of your codebase, not after something breaks.
2. Run it through your normal security scanner, not a lighter one
A 2022 Stanford study had 47 participants complete security-relevant coding tasks with and without an AI assistant. On the SQL injection task, 36% of the AI-assisted group wrote vulnerable code versus 7% of the control group. On the encryption task, 33% of the AI group shipped insecure solutions versus 21% without AI. Worse, participants using AI consistently rated their own code as more secure than the control group did, even when it wasn't — an inverse-trust problem that a scanner catches and a gut feeling doesn't.
3. Check the license and duplication risk
If a suggestion closely matches public code, you may be inheriting that code's license obligations along with it. GitHub's duplication detection filter blocks suggestions that match 65 lexemes or more of public code when an admin enables it — worth turning on for any repo where license provenance matters, and worth pairing with your own scanner if you're on a tool that doesn't offer one.
4. Know what you can and can't copyright
The U.S. Copyright Office’s March 2023 guidance reaffirmed that human authorship is required for copyright protection — a purely AI-generated function isn't copyrightable on its own. Your original selection, arrangement, and modification of that output generally is. If registration matters to your business, this is a legal distinction worth understanding before you file, not after.
5. Assign a human owner in the review, not just a bot
My walkthrough of the GitHub Copilot Autofix and Snowflake Jira incident is the clearest real-world case I've found: a human engineer wrote a vulnerable line, an AI review tool touched the same pull request without flagging it, and no one caught the gap before it shipped. Whoever approves the merge is the owner of record, full stop, regardless of how many tools reviewed it first.
6. Log why you accepted it
For anything non-trivial, a one-line commit note on why a suggestion was accepted as-is or modified saves real time later, when you're trying to reconstruct a decision during an incident review or a license audit.
Example prompts you can copy
These build the review habit into the session itself, rather than relying on catching problems after the fact:
- Before accepting: "List the security assumptions this code makes about its inputs, then flag anything you're not fully confident about."
- License check: "Does this pattern resemble a well-known open-source implementation? If so, name it and its license."
- Self-explain: "Explain this function back to me in two sentences before I accept it."
- Scope limiter: "Only touch the file I named. If the fix needs another file, stop and ask first."
- Adversarial review: "Review this diff as if you didn't write it — where would you push back before merging?"
Common mistakes to avoid
The biggest one, based on my own early habits: treating an AI-authored diff as lower-risk because it looked clean and well-formatted — the Stanford data above shows confident-looking AI code is exactly where security bugs hide. Second, skipping the same scanner you'd run on human code, on the assumption the assistant already checked itself; it didn't, unless you configured that explicitly. Third, assuming a vendor's indemnity policy covers you completely — most only apply to unmodified suggestions on specific paid tiers, and none of them cover a security incident caused by code you accepted and then shipped. Fourth, letting "the AI wrote it" become an unspoken excuse in a postmortem — my AI usage patterns in software teams piece covers how this shows up at the team level, not just individually. Fifth, never checking which plan you're actually on before assuming you have IP protection you don't.
Who's actually on the hook, by GitHub Copilot plan
| Plan | Price | IP indemnity for suggestions | Liable for bugs & security either way |
|---|---|---|---|
| Free | $0/mo | No | You |
| Pro | $10/mo | No | You |
| Pro+ | $39/mo | No | You |
| Business | Contact sales | Yes, for unmodified suggestions with filtering on | You |
| Enterprise | Contact sales | Yes, for unmodified suggestions with filtering on | You |
Pricing and indemnity terms confirmed on GitHub’s Copilot plans page as of September 1, 2026. Even on the two tiers with indemnity, it only covers copyright claims tied to unmodified suggestions — it says nothing about a vulnerability, a broken feature, or a compliance failure that ships because nobody reviewed the code closely enough.
Tools that make this easier
None of this requires switching what you use — it requires a review discipline layered on top of it. My AI coding without the vibes guide covers the spec-first, small-diff, read-every-line workflow that makes ownership practical instead of theoretical. If you're new to this category, AI coding assistant is a good primer, and best AI tool for code compares Cursor, Copilot, and Claude Code on the same tasks with real prices. For the GitHub-specific setup referenced above, see how to use GitHub Copilot in VSCode, and Cursor vs. Copilot if you're deciding between an AI-native editor and an extension. My honest review of AI programming goes deeper on where these tools genuinely help and where they don't.
My take
Ownership doesn't change because a suggestion came from an autocomplete box instead of your own fingers, and the tools themselves don't pretend otherwise — read GitHub's own terms and the responsibility sits with you in plain language. Where I see teams get burned isn't malice, it's speed: an AI-authored diff gets a faster, lighter review than a human one would, right up until the incident that proves it needed the same scrutiny all along. Review every line, run the same scanner you always run, and know your actual plan's terms before you assume a vendor has your back.
Frequently Asked Questions
Is AI-written code legally mine once I use it in my product?
Your original selection, arrangement, and modification of the output generally is protected, but the U.S. Copyright Office's 2023 guidance is clear that a purely AI-generated function isn't copyrightable on its own — human authorship is still the legal requirement.
Does GitHub Copilot protect me if AI-written code turns out to be insecure?
No. Copilot's IP indemnity on Business and Enterprise plans covers copyright claims tied to unmodified suggestions when filtering is enabled — it doesn't cover a security vulnerability, a bug, or a compliance failure in code you accepted and shipped.
Is AI-generated code actually less secure than human-written code?
In a controlled 2022 Stanford study, yes on the tasks tested: 36% of AI-assisted participants wrote SQL-injection-vulnerable code versus 7% of the control group, and participants trusted their AI-assisted code more even when it was less secure.
Who is responsible if an AI coding assistant introduces a bug that ships to production?
Whoever approved and merged the code — the human reviewer of record, not the tool. This holds regardless of which assistant wrote the original suggestion or whether an AI-powered review tool also looked at it.
Do I need different review standards for AI-written code versus human-written code?
No, and that's the point of this guide — the same scanner, the same review depth, and the same "I could explain this if asked" standard should apply either way. Lowering the bar for AI-authored diffs is where most of the real risk creeps in.