Two Approaches to Detecting AI -Generated Code

AI is now generating code at an unprecedented speed and scale, making it increasingly difficult for most engineering teams to distinguish between AI-generated and human-written contributions. Which is a real risk. 

Understanding how to detect AI-generated code, especially as it becomes standard in development, is about keeping your codebase traceable, secure, and maintainable.

Key Takeaways

• AI-generated code isn’t the problem; invisible AI code is. Visibility into where it exists in your codebase means you can review, secure, and maintain it properly.
  
  
• LLM code often exhibits statistical and structural patterns that detection models can learn to recognize.
  
  
• Detection makes for better reviews and safer releases. When teams know what’s AI-generated, they can add additional scrutiny, improve documentation, and reduce long-term technical risk.

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Why Detecting AI-Generated Code Matters More Than Ever

What started as a convenient autocomplete has evolved into a structural shift in how software gets built. Developers paste suggestions from Copilot, tweak snippets from ChatGPT, or let an LLM scaffold entire functions. 

And just like that, a codebase that was once fully human becomes a hybrid of human logic and machine-generated output, with no reliable record of which is which.

This isn’t a philosophical debate about “human vs. AI creativity” or about demonizing AI. It’s far more practical. AI-assisted code is everywhere, it’s useful, and it’s here to stay. The most pressing issue is that teams can’t see where it is.

This loss of visibility affects everything: code review, security, maintainability, and ownership. It introduces ambiguity into workflows that depend on clarity. And it forces engineering leaders to answer questions like: 

Who wrote this? 

What model was used? 

How trustworthy is this function? 

Which isn’t easy without a way to trace authorship.

Without provenance proof, it’s also hard to measure the effect AI code is having on code quality. Our longitudinal analysis shows teams moving faster in the GenAI era, while maintainability indicators trend downward.

AI Moves Work – But it Also Moves Risk

As AI blends deeper into development workflows, detection is an operational necessity. Not because AI code is inherently inferior, but because it behaves differently. It’s trained on snapshots of the internet, and it can replicate outdated security practices. It might hallucinate a missing edge case, or invent a dependency that doesn’t exist.

And because some developers may trust AI output more than they should, those issues can slip through code review unnoticed.

The more AI contributes, the more important it becomes to understand which parts of the codebase need closer scrutiny, and which parts simply need a metadata tag saying, “this is LLM code, review accordingly.”

Identifying AI-Generated Code

One of the challenges with AI-assisted code is how deceptively “normal” it looks. Often, it’s clean, well-formatted, and consistent. In fact, too consistent. 

You might notice:

• Repetitive structures where a human would introduce variation
• Comments that state the obvious
• Naming conventions that feel algorithmically uniform
• A function that mirrors code from a public repo a little too closely.

Individually, none of these signals are smoking guns. But together, they form a style fingerprint that today’s detection tools can recognize, and that engineering teams increasingly need.

Two Approaches to AI Code Detection 

Detection tools generally fall into two categories:

Retrospective Detection 

This analyzes code after it's written, using Machine Learning (ML) models trained on patterns in AI vs. human code. These tools scan existing codebases or new commits, flagging sections that match AI-generation signatures.

Provenance Tracking 

This captures AI authorship at the moment code is generated, through IDE plugins or version control hooks. Rather than inferring origin, these tools log which lines came from Copilot, ChatGPT, or other assistants in real-time.

Both have trade-offs. 

Provenance tracking offers certainty about which tool generated specific code, but requires upfront IDE integration, only works when developers use approved tools, and can't catch code pasted from external sources or identify AI code in existing repositories.

Retrospective detection works on any codebase (even legacy code), catches AI contributions regardless of source, and scales across entire organizations without requiring changes to developer workflows. But it depends on the quality and scale of the ML training data to maintain accuracy.

In enterprise environments, retrospective ML-based detection often provides broader coverage across legacy and multi-tool codebases. Again, the key differentiator is training data – systems trained on billions of commits can identify authorship patterns at scale, providing signals that support audit, compliance, and confident decision-making.

Detection provides visibility. But visibility alone doesn’t tell engineering leaders whether AI is improving productivity, increasing technical debt, or shifting effort into reviews and maintenance.

That’s why we recommend treating AI authorship as a measurable engineering signal, not just a classification exercise.

How to Bring AI Code Detection Into the Software Workflow

Start by evaluating different AI detection tools, looking for models that are validated across multiple engineering languages, not just optimized for a single best-case scenario. 

It’s also important to understand the tools producing most LLM code today, so you can get the most out of them. GitHub Copilot remains the most widely adopted and continues to generate a massive volume of code snippets. 

Bring your team on board, making sure everyone understands their role in maintaining code quality and authenticity. Teams that effectively manage AI-generated code tend to introduce a few simple habits:

• Making detection ambient. When code origin shows up automatically in PRs, reviewers know where to be extra cautious. 

• Setting clear expectations around LLM usage. Developers shouldn’t be guessing where AI is appropriate or when human-authored logic is required. 

• Documenting AI contributions lightly but consistently. A comment, a tag, a note in the PR – small signals that add up to big time savings during maintenance and incident response.

Flagging AI-generated segments early means:

• Reviewers know where to focus
• Security checks are more targeted
• Teams can document the presence of AI-authored code without slowing down development

Developers still get the speed boost of LLM assistance, but the organization keeps the oversight and accountability it needs.

Treat detection as a form of metadata: a signal that informs better decision-making. Then pair that signal with clear internal guidelines for how AI is used, eg; when to accept LLM suggestions, when extra review is needed, and when human-authored logic is non-negotiable.

The Future: Visibility with Objective Measurement

While we’re a long way off Anthropic CEO Dario Amodei’s predictions of 90% AI-authored code, LLMs will continue to write an increasing share of enterprise code. The question isn’t whether to use AI (that ship has sailed) but how to maintain visibility and quality in a world where AI is now a major contributor.

Success means knowing exactly where AI is in your codebase, and using that knowledge to build better processes, cleaner architectures, and more secure systems.

Detecting AI-generated code is the first step toward understanding how AI is reshaping software delivery. Organizations that move beyond detection to measure AI’s impact on effort, quality, and productivity will be best positioned to scale AI safely and effectively.

To help you get started, we’ve launched a free, no-setup AI Code Detector tool that quickly scans code and gives you a likelihood score for authorship. 

Try it here.