AI coding tools can now generate functions, fix bugs, write tests, explain unfamiliar code, and even produce large parts of an application.
For a developer, that can feel like a productivity breakthrough.
But there is another question worth asking before generated code reaches production:
Who is responsible for the legal risks attached to that code?
The answer is not always as simple as saying, "The AI wrote it."
The Developer Still Owns the Integration
When an AI tool generates a piece of code, a developer still decides whether that code enters the project.
That decision matters.
The developer may:
- Accept the generated code.
- Modify it.
- Combine it with existing code.
- Add dependencies around it.
- Deploy it into a production environment.
The AI system may have produced the suggestion, but the human development team controls how that suggestion becomes part of the software.
This makes review important not only from a security perspective, but also from a legal and compliance perspective.
The Hidden Risk of Code Similarity
One concern with AI-generated code is whether a generated snippet may resemble existing code.
That does not mean every similar-looking function creates a copyright problem. Short, functional code can raise different questions from substantial creative software components.
The practical issue for development teams is simpler:
Do you know where important code came from, and can you explain why it was used?
For small internal experiments, this may not receive much attention.
For commercial software, regulated products, or large engineering teams, provenance can become important.
Open-Source Licences Still Matter
AI assistance does not make open-source licensing requirements disappear.
Suppose generated code introduces a dependency or reproduces code associated with an open-source project.
The development team still needs to understand the applicable licence.
Different open-source licences can impose different conditions relating to:
- Attribution
- Copyright notices
- Distribution
- Modification
- Source-code availability
- Licence compatibility
A developer should therefore avoid treating an AI coding assistant as a substitute for dependency and licence review.
Security Review Is Only Half the Job
Security scanners can identify vulnerable packages, suspicious patterns, and known security issues.
They do not necessarily answer every legal question.
A code review process should therefore consider several separate questions:
Security:
Could this code create a vulnerability?
Licence:
Are the dependencies being used consistently with their licences?
Privacy:
Does the code collect, transmit, or expose personal information unnecessarily?
Provenance:
Can the team identify where important third-party components originated?
Documentation:
Can the team explain how the code entered the product?
These questions become increasingly important as AI-assisted development becomes part of ordinary engineering workflows.
Keep a Simple AI Development Record
Teams do not necessarily need a complicated bureaucracy.
For significant pieces of generated code, maintaining a lightweight record can help.
For example:
Feature: Payment validation
AI assistance: Used
Developer review: Completed
Third-party dependency review: Completed
Security review: Completed
Licence review: Completed
Production approval: Developer A
The purpose is not to document every autocomplete suggestion.
The purpose is to create accountability around material software decisions.
Don't Put Sensitive Code Into an AI Tool Without Checking the Rules
There is another issue developers sometimes overlook.
An AI coding assistant may process the information supplied to it.
Before submitting source code, configuration files, customer information, credentials, proprietary algorithms, or internal documentation, developers should understand the tool's data-handling terms and their organisation's policies.
A five-second coding shortcut should not create a much larger confidentiality problem.
Never paste:
- API keys
- Passwords
- Private certificates
- Customer databases
- Confidential contracts
- Proprietary source code
into an AI system simply because the tool makes debugging easier.
AI Assistance Does Not Replace Engineering Judgment
The most useful way to think about AI-generated code is not as "safe" or "unsafe" by default.
It is another source of code that requires appropriate review.
A developer should be able to ask:
Would I approve this code if a junior developer submitted it?
If the answer is no, the fact that an AI generated it does not change the review requirement.
The Practical Rule
AI coding tools can make development faster.
They do not automatically make software legally compliant.
Before shipping significant AI-assisted code, developers and engineering teams should consider security, licensing, privacy, provenance, confidentiality, and human review.
The strongest development workflow is not one where AI writes the most code.
It is one where developers remain accountable for the code that ultimately reaches users.
*Disclaimer: *
This article is for general legal and technology awareness and does not constitute legal advice. Specific copyright, licensing, privacy, or contractual questions should be reviewed according to the applicable law, licence terms, and facts of the project.
Peesh Chopra
Advocate | Legal Writer
Exploring law, technology, digital evidence, privacy, public justice, and legal awareness.
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