Mitigate Intellectual Property (IP) and Copyright Risks from AI-Generated Code
Proactively mitigate the legal and intellectual property (IP) risks associated with AI-generated code. Models trained on public repositories may generate code that is "derivative" of existing copyrighted or copyleft-licensed software, creating a "copyright infringement" risk. This could inadvertently "stain" a proprietary codebase with a restrictive license (e.g., GPL), creating a significant legal and business liability.
Establish a formal strategy, in partnership with Legal, to mitigate the IP and copyright risks of AI-generated code. This strategy must include: 1) prioritizing AI tools that provide "legal clarity" on their training data, 2) enforcing Software Composition Analysis (SCA) in the CI/CD pipeline to check for license compliance, and 3) conducting a legal review of tool-vendor indemnity policies.
There are two primary IP risks with AI-generated code: Copyright Infringement (Inbound): AI models trained on public repositories (which include GPL, AGPL, and other restrictive licenses) "can assemble code that is very similar to existing software without adhering to licensing terms". If your developer accepts this code, your organization's proprietary product may now be a "derivative work" of a copyleft-licensed project, creating a massive legal obligation you cannot meet. Data Leakage (Outbound): As described in Rec 22, if your own "sensitive information included in training data (such as proprietary codebases) may be reproduced in generated AI outputs" for other companies, you are actively leaking your own IP.
This is a mandatory consideration for any organization that builds and sells proprietary, closed-source software. This risk analysis must be performed during the tool selection process (Rec 15). This should be reviewed annually with the Legal department as part of the governance/ai-governance-scorecard review.
This is a three-part mitigation strategy: Prioritize Tools with "Legal Clarity": This risk must be a central criterion in your Tool Selection Matrix (Rec 15). The "Model Provider" and "Training Data Source" criteria are not just about quality; they are about legal indemnity. Strongly consider tools (like Tabnine) that are "purpose-built for enterprises" and whose "proprietary models [are] trained exclusively on permissively licensed open source". This "legal clarity" is their primary value proposition. Conversely, for tools (like Copilot) that are trained more broadly, your Legal team must review their indemnity policies and determine if the organization accepts the ambiguity. Enforce Technical Guardrails (Rec 17): The AI-augmented CI/CD pipeline (Rec 17) must include a robust Software Composition Analysis (SCA) scanner. This scanner is the technical guardrail that checks for both security vulnerabilities in dependencies and license compliance, flagging any code (AI-generated or not) that introduces a restrictive license. Partner with Legal: The "cross-functional AI leads" (Rec 22) must include Legal. Legal must review and approve the selected AI tool and its terms of service. Legal must help define the SCA policies (e.g., "Block all GPL/AGPL licenses"). Legal must be involved in the governance/ai-governance-scorecard to assess the organization's overall risk tolerance for code-origin ambiguity.
- AI-generated Code: How to Protect Your Software From AI ... - https://www.ox.security/blog/ai-generated-code-how-to-protect-your-software-from-ai-generated-vulnerabilities/
AI models trained on public repositories "can assemble code that is very similar to existing software without adhering to licensing terms". - Managing Data Security and Privacy Risks in Enterprise AI | Frost Brown Todd - https://frostbrowntodd.com/managing-data-security-and-privacy-risks-in-enterprise-ai/
If your own "sensitive information included in training data (such as proprietary codebases) may be reproduced in generated AI outputs" for other companies, you are actively leaking your own IP. - Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile - NIST Technical Series Publications - https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
NIST framework for managing AI risks, including IP and copyright risks from AI-generated code.
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