{"ID":2879036,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16853","arxiv_id":"2508.16853","title":"DevLicOps: A Framework for Mitigating Licensing Risks in AI-Generated Code","abstract":"Generative AI coding assistants (ACAs) are widely adopted yet pose serious legal and compliance risks. ACAs can generate code governed by restrictive open-source licenses (e.g., GPL), potentially exposing companies to litigation or forced open-sourcing. Few developers are trained in these risks, and legal standards vary globally, especially with outsourcing. Our article introduces DevLicOps, a practical framework that helps IT leaders manage ACA-related licensing risks through governance, incident response, and informed tradeoffs. As ACA adoption grows and legal frameworks evolve, proactive license compliance is essential for responsible, risk-aware software development in the AI era.","short_abstract":"Generative AI coding assistants (ACAs) are widely adopted yet pose serious legal and compliance risks. ACAs can generate code governed by restrictive open-source licenses (e.g., GPL), potentially exposing companies to litigation or forced open-sourcing. Few developers are trained in these risks, and legal standards var...","url_abs":"https://arxiv.org/abs/2508.16853","url_pdf":"https://arxiv.org/pdf/2508.16853v1","authors":"[\"Pratyush Nidhi Sharma\",\"Lauren Wright\",\"Anne Herfurth\",\"Munsif Sokiyna\",\"Pratyaksh Nidhi Sharma\",\"Sethu Das\",\"Mikko Siponen\"]","published":"2025-08-23T00:51:29Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.AI\"]","methods":"[]","has_code":false}