AI coding assistants have moved from novelty to default. A recent GitHub developer survey found that the overwhelming majority of professional developers have used an AI coding tool at some point in their work. Tools like Copilot, Cursor, Windsurf, Claude, and others have fundamentally changed how code gets written. At the same time, they've also introduced a category of intellectual property and open source license compliance risk that many organizations aren't yet equipped to manage.
Developers lean on AI coding tools in two fairly distinct ways:
Autocomplete-style assistance. This is the "traditional" Copilot experience: as a developer types, the model suggests the next few lines, or sometimes an entire function, based on the surrounding context. The developer reviews the suggestion, accepts it (often with a single keystroke), and moves on.
Conversational and agentic assistance. Here, a developer (or increasingly, an autonomous coding agent) describes a problem at a higher level. The AI tool then decides how to solve it. Critically, that decision isn't limited to writing original logic. AI tools frequently determine that the best path forward is to pull in an existing open source package, and in agentic workflows, they may install that dependency directly, with little or no human involvement before code lands in a branch or even a build.
Both patterns are productivity wins. Both also create open source license compliance exposure that looks nothing like the risk profile most legal and engineering teams built their compliance programs around.
Legal Risks from AI-Generated Snippets
Code-generating models are trained on enormous volumes of publicly available source code. Much of it is pulled from public repositories that carry open source licenses, including copyleft licenses like the GPL and AGPL that impose real obligations (such as source disclosure or "share-alike" requirements) on anyone who incorporates that code into their own software. The model isn't reasoning about license terms; it's learning statistical patterns from that corpus and generating output token by token based on what's likely to come next.
The problem is that "likely to come next" can, in some cases, mean reproducing a sequence the model has effectively memorized, particularly for code that's heavily duplicated across the training set, or distinctive enough that there's really only one common way to write it.
GitHub itself has acknowledged that a small percentage of Copilot suggestions over a certain length can match the training data closely enough to be flagged by its own duplication-detection filter. When that happens, the developer accepting the suggestion has no visibility into where that code came from, what license governs the file it was lifted from, or what obligations might now attach to their own codebase.
The legal landscape here is still developing, but the practical lesson for engineering and legal teams is already clear: open source licenses don't stop applying just because an AI model, rather than a human, did the typing.
This matters operationally because AI-generated snippets are largely invisible to traditional open source license compliance tooling. Conventional software composition analysis (SCA) tools work by scanning your dependency manifests and lockfiles; they're built to catch things you added, not code that was typed directly into a file with no package reference at all. A few lines pasted in by an AI assistant don't show up in a package.json or go.mod. They just become part of your source code, with whatever licensing obligations they carry along for the ride, undetected.
Legal Risks When AI Pulls in a Full Package
Snippet-level risk is real, especially with AI tools that lack license-aware guardrails or duplicate-detection capabilities. But the risk profile changes substantially when a developer asks an AI tool to solve a problem and the AI's answer is "use this open source package" — and the developer (or the agent acting on their behalf) goes ahead and adds it.
A generated snippet might carry the licensing baggage of a few lines of one file. A full dependency carries the licensing baggage of an entire project, plus whatever its own transitive dependencies bring with them. While there’s still some ambiguity about the true legal risk of small code snippets, the courts have been clear about licensing requirements for full dependencies.
What makes this scenario particularly dangerous from a compliance standpoint isn't just the size of the risk; it's where it enters your pipeline. Most organizations with a mature OSS compliance program have a defined intake path: new dependencies get scanned, checked against license policy, and reviewed before they're approved for use. That process assumes a human deliberately chose to add a dependency, typically through a package manager command that shows up in a manifest file destined for a pull request.
AI-assisted and agentic workflows don't necessarily respect that path. A developer chatting with an AI tool, or an autonomous coding agent working through a task, can decide on and install a package in the middle of a session. This may be part of rapid prototyping that later gets shipped with little additional scrutiny. There’s also a scenario where an AI agent actually adds and installs a dependency with minimal or even no human review (particularly for potential licensing conflicts).
If your compliance program's primary checkpoint is "we review what shows up in pull requests," and the dependency arrived through an AI-driven decision that happened upstream of that checkpoint, you have a real chance of it evading your policy entirely until it's already in production, or until it surfaces in an audit, an acquisition due diligence process, or a license compliance dispute.
Managing Risks from AI-Produced Snippets and Full Dependencies
At its core, the operational concern for teams focused on license compliance is one of coverage. Organizations don't need an entirely new compliance philosophy to manage AI-related IP risk. Rather, they need their existing license compliance program extended to see what it currently can't.
That's the thinking behind FOSSA's approach to this challenge. On the snippet side, FOSSA's snippet scanning capability analyzes code at the function and expression level and matches it against a database of known open source components, regardless of variable renaming or formatting changes. This means AI-generated code that was never deliberately copied from anywhere, and never touched a package manager, can still be traced back to its open source origin, its governing license, and any associated obligations.
On the dependency side, FOSSA's core license compliance engine continues to do what it's always done: build a complete dependency graph, identify every license in play, and enforce policy automatically. This applies regardless of whether that dependency was added because an engineer searched for it deliberately or because an AI assistant suggested it mid-conversation. The point of origin doesn't change the obligation.
The real value comes from running both as part of one workflow rather than two disconnected tools. Snippets and dependencies show up in the same inventory, get evaluated against the same license policies, and feed the same attribution reports and SBOMs. Engineering teams don't have to slow down or second-guess every AI suggestion, and legal and compliance teams don't have to choose between enabling AI-assisted development and maintaining an accurate picture of what's actually in their software.
If you want to discuss this topic in more depth (and/or see a demo of our AI Guardrails solution), you can reach out to me: aaron@fossa.com.