Copyright and Risks of AI-Generated Code: OSS Licenses, Copyleft, and Enterprise Best Practices
About 5 minutes
As AI-assisted coding (GitHub Copilot, Amazon Q Developer, and others) becomes widespread, enterprises need to understand the legal risks spanning copyright law, OSS licenses, and patent law before adopting these tools [1][2][3]. This article organizes the risks specific to AI-generated code and the measures enterprises should take.
Copyright Ownership of AI-Generated Code
Section titled “Copyright Ownership of AI-Generated Code”Does AI-Generated Code Have Copyright?
Section titled “Does AI-Generated Code Have Copyright?”Under Japanese copyright law, a work is defined as “a creative expression of thoughts or feelings,” and human creative authorship is a prerequisite [1]. Code autonomously generated by AI without any human creative contribution is generally considered unlikely to qualify for copyright protection.
In practice, however, it is rare for AI to generate code with complete autonomy. Humans participate in the creative process in the following ways:
- Designing and iteratively refining prompts
- Selecting the best option from multiple generated candidates
- Modifying, refactoring, and combining generated code
The prevailing interpretation is that the portions reflecting human judgment, editing, and creative selection may give rise to copyright [1].
Who “Owns” the Code?
Section titled “Who “Owns” the Code?”| Party | Nature of Rights |
|---|---|
| AI tool vendor | Output use conditions, indemnity, training-data use, and log handling depend on the service’s terms and administrative controls [2][3] |
| User (individual or enterprise) | Portions with human creative contribution may be protectable, while autonomously generated AI portions have a limited protection scope [1] |
| Copyright holders of training data | May have grounds to assert rights if generated code is derived from or substantially similar to the training data [1] |
Tool-selection premise: Services such as GitHub Copilot and Amazon Q Developer differ in features, administrative controls, and treatment of output [2][3]. Rights, indemnity, code referencing or duplicate detection, and handling of input data must be checked against the official documentation and contract terms in effect at adoption time.
OSS License Issues: Open-Source Code as Training Data
Section titled “OSS License Issues: Open-Source Code as Training Data”The Nature of the Problem
Section titled “The Nature of the Problem”When using generative AI coding tools that may learn from or reference public code, teams need to verify which license conditions may affect inputs, outputs, and candidate code. Public repositories include code under permissive licenses such as MIT and Apache 2.0, but also code under copyleft licenses such as GPL, LGPL, and AGPL [4].
Training Data
├── MIT-licensed code (permissive: free to reuse and use commercially)
├── Apache 2.0-licensed code (permissive: includes patent clause)
├── GPL code (copyleft: same license propagates to derivatives)
├── LGPL code (weak copyleft: conditions relaxed for dynamic linking)
└── AGPL code (strong copyleft: also applies to network-served software)The Copyleft “Propagation” Risk
Section titled “The Copyleft “Propagation” Risk”The core condition of the GPL license is that when software incorporating GPL code is distributed, the corresponding source code must be made available under GPL conditions [4].
Risk scenario with AI-generated code:
- AI outputs code substantially similar to GPL code
- A developer incorporates that code into a proprietary product
- The GPL conditions “propagate,” potentially requiring release of the entire product’s source code
This “copyleft contamination” is a risk that directly impacts enterprise intellectual property strategy [4].
Assessing Substantial Similarity
Section titled “Assessing Substantial Similarity”Copyright infringement requires both access (provenance) and substantial similarity, and for code, similarity is evaluated on these dimensions:
- Literal similarity: Code copied verbatim or near-verbatim
- Non-literal similarity: Similarity in code structure, algorithms, or program flow
Note that ideas and algorithms themselves are not protected by copyright (the idea-expression dichotomy). Code that independently implements the same algorithm with a different expression does not constitute copyright infringement [1].
Patent Rights: Implementation Risk of Algorithms
Section titled “Patent Rights: Implementation Risk of Algorithms”Separately from copyright, code generated by AI may infringe existing patents covering the technology or process it implements [5].
- Even if AI-generated, code that practices a patented invention constitutes patent infringement [5]
- Whether the generation process was automated does not affect the patent infringement determination
- Patent investigation (Freedom to Operate analysis) is required separately from copyright checks
Patent investigation of generated code is especially important in high-patent-density fields such as healthcare, finance, telecommunications, and security.
Security Risks: Cautions Beyond Copyright
Section titled “Security Risks: Cautions Beyond Copyright”AI-generated code can also contain security issues.
Problems Reported in Academic Research
Section titled “Problems Reported in Academic Research”Research published in 2021 found that code generated by GitHub Copilot can contain security vulnerabilities [6].
| Risk Type | Example |
|---|---|
| Use of outdated or vulnerable APIs | Deprecated cryptographic methods, deprecated functions |
| SQL injection | Improper query construction |
| Authentication weaknesses | Missing permission checks, incomplete input validation |
| Sensitive information leakage | Hardcoded credentials or secrets |
| OS command injection | Command execution involving external input |
Generative AI produces code that “looks right,” but does not guarantee security correctness [6].
Practical Enterprise Responses
Section titled “Practical Enterprise Responses”Key Factors in Tool Selection
Section titled “Key Factors in Tool Selection”| Item to Verify | Reason |
|---|---|
| Training-data and referenced-code license policy | Assess copyleft contamination risk [2][3][4] |
| Rights assignment and output use conditions | Confirm the scope in which the enterprise can use the code [2][3] |
| Availability of copyright indemnification programs | Confirm the protection scope if infringement claims arise [2][3] |
| Enterprise-grade options | Manage input data, logs, access control, and code-reference detection [2][3] |
Designing a Code Generation Policy
Section titled “Designing a Code Generation Policy”Enterprise Code Generation Policy (Example)
1. Permitted Tools
- Use only approved generative AI tools
- Inputting code with personal accounts is prohibited (prevent leakage of sensitive code)
2. Mandatory Code Review
- All generated code must go through human code review
- Code critical to security (authentication, encryption, etc.) requires expert review
3. OSS Compliance
- Scan generated code with an OSS scanning tool before committing
- When copyleft licenses are detected, consult legal or IP departments
4. Prompt Management
- Do not include sensitive information, customer data, or internal system details in prompts
- Maintain a record of the correspondence between prompts used and generated codeImplementing OSS Scanning
Section titled “Implementing OSS Scanning”Tools used to detect license contamination in generated code:
- FOSSA / Black Duck / Snyk — commercial OSS compliance tools
- ScanCode / FOSSology — open-source scanning tools
- Code reference or duplicate-detection features provided by AI tool vendors, such as GitHub Copilot code referencing [2]
Record Keeping
Section titled “Record Keeping”Maintaining the following information about generated code can assist in responding when issues arise later.
Information to Record
- Name and version of the AI tool used
- Overview of the prompt used for generation
- Date and time of generation
- Code reviewer and review date
- OSS scan results and actions takenSummary
Section titled “Summary”Legal risks in AI-generated code extend beyond copyright to OSS licenses, patents, and security.
| Risk | Description | Primary Response |
|---|---|---|
| Copyright | Ambiguity in rights ownership; similarity derived from training data | Verify terms of service; verify indemnification programs |
| Copyleft contamination | Risk of GPL and similar licenses propagating to the entire enterprise product | OSS scanning; copyleft isolation policy |
| Patent rights | Risk that generated code infringes existing patents | FTO analysis; caution in high-patent-density areas |
| Security | Automatic generation of vulnerable code | Mandatory code review; security scanning |
Generative AI can dramatically improve development productivity, but recognizing these risks and designing governance around them is a prerequisite for sustainable enterprise adoption.
References
Section titled “References”- e-Gov Law Search, Copyright Act (Act No. 48 of 1970)
- GitHub Docs, GitHub Copilot features
- AWS, Amazon Q Developer
- GNU Project, GNU General Public License version 3
- e-Gov Law Search, Patent Act (Act No. 121 of 1959)
- Pearce et al., Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions, arXiv:2108.09293 (2021)