AI's (and your) copyright blind spot

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You're using Cursor, your team loves GitHub Copilot, and someone just discovered that Claude can write entire functions in seconds.

But what happens when that generated code isn't actually "generated" but memorized from someone else's copyrighted work?

Last month, I spoke with a CTO who discovered their AI-generated authentication module contained nearly identical logic to a GPL-licensed library. The legal team flagged it during a routine IP audit before a major client deal. Crisis averted, but barely.

This is no longer a theoretical problem. It's happening now, and most companies aren't prepared for it.

Traditional software development has clear paper trails. Your team writes code, uses licensed libraries, and documents dependencies. You know what you're using and how to use it effectively.

AI code generation completely breaks this model.

When an AI suggests code, you're seeing the output of a system trained on millions of repositories: open source, proprietary, copyleft, and everything in between. The model doesn't distinguish between MIT-licensed code, which can be freely reproduced, and proprietary code, which cannot.

It just predicts what comes next.

The result?

You may be using patented algorithms, GPL-licensed code, or direct copies of proprietary systems without realizing it.

And "the AI did it" isn't a legal defense.

Three Scenarios Every Leader Should Understand

Scenario 1: The Exact Match. Your AI generates code that's nearly identical to a copyrighted work. It could be a specific algorithm, a unique data structure, or even a particularly clever solution to a common problem. If the original work is under a restrictive license, you've inherited that license and its obligations.

Scenario 2: The Patent Trap. AI generates an implementation of a patented process. Patents don't require access to source code. Even if your AI independently "discovers" a patented method, you still need a license to use it commercially.

Scenario 3: The Viral License. Your AI incorporates logic from GPL or other copyleft-licensed code. These licenses (generally) require derivative works to be released under the same terms. One small AI-generated function could force you to open-source your entire application.

Building Legal Guardrails That Work

Start with clear policies. Establish the level of acceptable AI assistance for different types of projects. Critical infrastructure might require human-written code with minimal AI assistance. Prototypes might allow more aggressive AI use with the understanding that all code will be reviewed before production. Caveat: This requires additional effort for code review before release.

Implement technical safeguards. Tools like GitHub's code scanning can identify when generated code matches known repositories. Some organizations run AI-generated code through plagiarism detection systems designed for software. These aren't perfect, but they catch obvious problems.

Develop review processes that consider AI-generated content. Your code reviews should specifically flag AI-generated sections for further review and scrutiny. Train reviewers to recognize common patterns in AI-generated code and to question anything that seems too sophisticated for the context.

The Documentation Challenge

Here's a problem most companies haven't considered: How do you document AI-generated code for future legal review?

Traditional code has comments, commit messages, and development history that explain decisions and dependencies. AI-generated code often appears fully formed with minimal context.

Establish documentation standards for AI assistance. When was AI used? What prompts were given? What alternatives were considered? This paper trail becomes crucial if legal questions arise later.

Consider implementing "AI tags" in your codebase. These tags track which sections were AI-generated, when, and by which tool. This makes future audits easier and demonstrates a commitment to legality.

The Strategic View

Companies that safely solve AI code generation will move faster than those that are paralyzed by IP concerns. But companies that ignore these risks entirely could face expensive legal challenges later.

The most sophisticated organizations I work with treat this like any other technology risk. They're not asking whether to use AI code generation but rather how to use it within acceptable risk parameters.

They're also thinking about competitive advantage. While competitors worry about IP exposure, they build safe AI workflows that accelerate development without legal risk.

What This Means for Your Organization

If your teams are using AI code generation tools (and they probably are, whether you know it or not), you need IP policies that address this reality.

Start with an audit. What AI tools are your developers using? How much AI-generated code is in your current projects? What's your exposure if legal challenges arise?

Build the framework. This includes clear policies, technical safeguards, review processes, and documentation standards. You can’t delegate it entirely to legal or engineering…it needs a business strategy.

You aren’t trying to eliminate risk but to manage it intelligently while capturing the productivity of AI assistance.

The technology isn't going away, and the legal questions aren't getting simpler.

P.S. Your legal team probably hasn't thought about this systematically yet. Most haven't. But the first time you face an IP claim involving AI-generated code, they'll wish you had started this conversation sooner.

This challenge lies at the intersection of technology strategy, legal risk, and competitive advantage. While I am not a lawyer, I can help you work through the technical aspects of these issues. ericbrown.com.

If you found this post helpful, consider sharing it with another executive grappling with AI, technology, and data. If you want to explore AI and other Technology strategies, grab some time on my calendar, and let's chat.

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