Global copyright risk governance for generative AI: theory and framework

  • Yusuf Gunawan 
    IBLAM School of Law
    Indonesia
  • Deborah Sarah Najoan
    IBLAM School of Law
    Indonesia
  • Widodo Widodo
    IBLAM School of Law
    Indonesia

Abstract

The computational training of Generative Artificial Intelligence (Generative AI) relies on the large-scale ingestion of multi-modal datasets that may routinely incorporate copyrighted works without prior authorization or a clearly applicable statutory exception. Classical copyright paradigms, anchored in individualized ex post copyright litigation, exhibit structural strain when confronting the systemic risks, information asymmetries, and regulatory arbitrage generated by algorithmic opacity. This study addresses three core questions: (1) why conventional individualized ex post copyright enforcement is inadequate for foundation model training; (2) how regulatory risk theory can reconceptualize copyright exposure in AI ecosystems; and (3) how such a model can be operationalized internationally. Employing doctrinal legal research, functional comparative regulatory analysis, and conceptual synthesis, this article develops the Global Copyright Risk Governance Theory (GCRGT). GCRGT conceptualizes copyright friction in Generative AI not merely as discrete private disputes, but as a systemic regulatory risk requiring adaptive, polycentric governance. To operationalize this theoretical framework, the article constructs the Global Copyright Risk Governance Framework (GCRGF) a nine-stage recursive governance cycle integrating Copyright Risk Impact Assessments (CRIAs), machine-readable rights reservation standards, risk-tiered compliance mechanisms, independent technical auditing, and post-deployment monitoring and remediation. Examining the European Union AI Act alongside regulatory patterns in the United States, the United Kingdom, Japan, Singapore, and China, the framework sets out a pathway for transitioning copyright governance from reactive judicial remedies toward proactive, risk-based, and globally coordinated oversight.

Keywords
  • Copyright law
  • EU AI act
  • Foundation models
  • Generative artificial intelligence
  • Intellectual property
  • Polycentric governance
  • Risk-based regulation
Lisensing Information
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Gunawan, Y., Najoan, D. S., & Widodo, W. (2026). Global copyright risk governance for generative AI: theory and framework . Lentera Negeri, 7(1), 2322–2330. https://doi.org/10.29210/993540
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