Developing a Risk-Based Intellectual Property Governance Framework for Generative Artificial Intelligence: A Comparative Analysis of the Eu Ai Act, Uk, and Us Legal Systems
DOI:
https://doi.org/10.54536/ajsl.v5i2.8527Keywords:
Comparative Law, Copyright, Generative Artificial Intelligence, Governance, Intellectual Property, RiskAbstract
This paper develops a risk-based intellectual property (IP) governance framework for generative artificial intelligence (GenAI) through doctrinal, comparative, and normative analysis of the European Union (EU), the United Kingdom (UK), and the United States (US). It covers copyright and related IP challenges in data acquisition, model training, output creation, and commercial deployment, as well as the distribution of control, transparency, attribution, and liability in each jurisdiction. The analysis reveals that the EU has several text-and-data-mining exceptions and copyright-policy and training-content-summary obligations for general-purpose AI providers, while the UK has a very limited non-commercial research exception and a unique statutory exception for computer-generated works, and the US is driven primarily by the human-authorship doctrine and fact-specific fair-use analysis. While these strategies offer valuable protections, none alone establish a comprehensive system of organisational control. The study thus suggests an auditing framework linked to the legal basis, data provenance, licenses, human contribution to creativity, output testing, documentation, accountability and escalation, and periodic reassessment, according to the proportionate degree of risk. Rather than harmonizing the three legal systems, the framework sets out a common organizational process for identifying and managing differences in the three legal systems to safeguard the rights holders and to allow for responsible innovation.
Downloads
References
Ahern, D. (2025). The new anticipatory governance culture for innovation: Regulatory foresight, regulatory experimentation and regulatory learning. European Business Organisation Law Review. https://doi.org/10.1007/s40804-025-00348-7
Alabi, M. (2023). (PDF) data governance and quality: Ensuring data reliability and trustworthiness. ResearchGate. https://www.researchgate.net/publication/384728792_Data_Governance_and_Quality_Ensuring_Data_Reliability_and_Trustworthiness
Al-Busaidi, A.S., Raman, R., Hughes, L., Albashrawi, M.A., Malik, T., Dwivedi, Y.K., Al- Alawi, T., AlRizeiqi, M., Davies, G., Fenwick, M., Gupta, P., Gurpur, S., Hooda, A., Jurcys, P., Lim, D., Lucchi, N., Misra, T., Raman, R., Shirish, A. & Walton, P. (2024). Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights. Journal of Innovation & Knowledge, 9(4), 100630. https://doi.org/10.1016/j.jik.2024.100630.
Amjad, I. (2026). AI copyright infringement: Navigating the legal risks of AI-Generated content. Trends in Intellectual Property Research, 4(1), 24–28. https://doi.org/10.69971/tipr.4.1.2026.104.
Ashikuzzaman, M. (2025). What is copyright? Library & Information Science Education Network. https://www.lisedunetwork.com/what-is-copyright/.
Atadjanov, R. (2026). Interpretation of Law. A Common Theory of Law, 147–153. https://doi.org/10.1007/978-94-6265-711-3_11
Atilla, S. (2024). Dealing with AI-generated works: Lessons from the CDPA section 9(3). Journal of Intellectual Property Law & Practice, 19(1), 43–54. https://doi.org/10.1093/jiplp/jpad102
Awad, T. (2022). Universalizing copyright fair use: To copy, or not to copy? Digital Commons @ University of Georgia School of Law. https://digitalcommons.law.uga.edu/jipl/vol30/iss1/2/
Axelsson, S. (2025). AI training in a legal patchwork - examining generative AI training on images under EU copyright, data protection, and personality rights. Lub.lu.se. https://lup.lub.lu.se/student-papers/search/publication/9217041.
Ben-Daniel, D. O. (2025). Impact of Generative Artificial Intelligence on The Global Entertainment Industry & Its Legal Dynamics. American Journal of Society and Law, 4(1), 46-54. https://doi.org/10.54536/ajsl.v4i1.4566
Bhatti, B. (2026). The Regulation of Artificial Intelligence in the Legal Profession. American Journal of Society and Law, 5(1), 1-9. https://doi.org/10.54536/ajsl.v5i1.7210
Buick, A. (2025). Copyright and AI training data—Transparency to the rescue? Journal of Intellectual Property Law & Practice, 20(3), 182–192. https://doi.org/10.1093/jiplp/jpae102
Chan, K.J.D., Papyshev, G. & Yarime, M. (2024). Balancing the tradeoff between regulation and innovation for artificial intelligence: An analysis of top-down command and control and bottom-up self-regulatory approaches. Technology in Society, 79, 102747. https://doi.org/10.1016/j.techsoc.2024.102747.
Chen, G., Tong, Y. & Han, Z. (2026). Comparative study on the ownership of copyright of artificial intelligence-generated works. Laws, 15(4), 66. https://doi.org/10.3390/laws15040066.
Chesterman, S. (2025). Good models borrow, great models steal: Intellectual property rights and generative AI. Policy and Society, 44(1), 23–37. https://doi.org/10.1093/polsoc/puae006
Citizens Take Over Europe (2026). EU vs. US approaches to AI regulation - citizens take over Europe. Citizens Take Over Europe. https://citizenstakeover.eu/blog/eu-vs-us-approaches-to-ai-regulation/.
de la Durantaye, K. (2025). Control and compensation: A comparative analysis of copyright exceptions for training generative AI. IIC - International Review of Intellectual Property and Competition Law, 56, 737–770. https://doi.org/10.1007/s40319-025-01569-6
Dermawan, A. (2024). AI v copyright: How could public interest theory shift the discourse? Journal of Intellectual Property Law & Practice, 19(1), 55–63. https://doi.org/10.1093/jiplp/jpad111
Diab, M. F. S. (2024). Criminal Liability for Artificial Intelligence and Autonomous Systems. American Journal of Society and Law, 3(1), 14-18. https://doi.org/10.54536/ajsl.v3i1.2481
Dornis, T. W. (2025). Generative AI, reproductions inside the model, and the making available to the public. IIC - International Review of Intellectual Property and Competition Law, 56, 909–938. https://doi.org/10.1007/s40319-025-01582-9
Drori, I., & Te’eni, D. (2024). Human-in-the-loop AI reviewing: Feasibility, opportunities, and risks. AIS Electronic Library (AISeL). https://aisel.aisnet.org/jais/vol25/iss1/7/.
Elmahjub, E. (2025). The algorithmic muse and the public domain: Why copyright’s legal philosophy precludes protection for generative AI outputs. Computer Law & Security Review, 58, 106170. https://doi.org/10.1016/j.clsr.2025.106170
European Commission. (2024). Template for general-purpose AI model providers to summarise their training content. Europa.Eu. https://digital-strategy.ec.europa.eu/en/faqs/template-general-purpose-ai-model-providers-summarise-their-training-content
Farhad, M. A., & Zakir, M. H. (2024). Adapting legal horizons in reshaping intellectual property law for the artificial intelligence revolution. AI and Ethics. https://doi.org/10.1007/s43681-024-00555-x.
Farhat, A. (2025, November 9). EU AI act 2026: New rules for training data and copyright. Scalevise Resources. https://scalevise.com/resources/eu-ai-act-2026-changes/
Gaffar, H. & Albarashdi, S. (2024). Copyright protection for AI-Generated works: Exploring originality and ownership in a digital landscape. Asian Journal of International Law, 14(2), 1–24. https://doi.org/10.1017/s2044251323000735.
Gandhi, D., Joshi, H., Hartman, L. & Hassani, S. (2025). Approaches to responsible governance of GenAI in organizations. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2504.17044.
Geiger, C. (2024). Elaborating a human rights-friendly copyright framework for generative AI. IIC - International Review of Intellectual Property and Competition Law, 55, 1129–1165. https://doi.org/10.1007/s40319-024-01481-5
GOV.UK. (2026). Report on copyright and artificial intelligence - GOV.UK. Www.Gov.Uk. https://www.gov.uk/government/publications/report-and-impact-assessment-on-copyright-and-artificial-intelligence/report-on-copyright-and-artificial-intelligence
Habbal, A., Ali, M.K. & Abuzaraida, M.A. (2024). Artificial Intelligence Trust, risk and security management (AI TRiSM): Frameworks, applications, challenges and future research directions. Expert Systems with Applications, 240(122442), 122442. https://doi.org/10.1016/j.eswa.2023.122442.
Hall, J. (2026). Why Getty Images v Stability AI got secondary copyright infringement wrong. Journal of Intellectual Property Law & Practice, 21(8), 538–545. https://doi.org/10.1093/jiplp/jpag074
Hayes, C.M. (2023). Generative Artificial Intelligence and copyright: Both sides of the black box. Social Science Research Network. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4517799.
Huang, K., Joshi, A., Dun, S. & Hamilton, N. (2024). AI regulations. Future of business and finance, pp.61–98. https://doi.org/10.1007/978-3-031-54252-7_3.
Hutson, J. (2024). The evolving role of copyright law in the age of AI-Generated works. Journal of Digital Technologies and Law, 2 (eng)(4). https://cyberleninka.ru/article/n/the-evolving-role-of-copyright-law-in-the-age-of-ai-generated-works.
Intellectual Property Office. (2024, December 17). Copyright and artificial intelligence. GOV.UK. https://www.gov.uk/government/consultations/copyright-and-artificial-intelligence/copyright-and-artificial-intelligence
Janssen, M. (2025). Responsible governance of generative AI: Conceptualizing GenAI as complex adaptive systems. Policy and Society, 44(1), 38–51. https://doi.org/10.1093/polsoc/puae040
Kahveci, Z. Ü. (2023). Attribution problem of generative AI: A view from US copyright law. Journal of Intellectual Property Law & Practice, 18(11), 796–807. https://doi.org/10.1093/jiplp/jpad076
Kassa, B.Y. & Worku, E.K. (2025). The impact of artificial intelligence on organizational performance: The mediating role of employee productivity. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100474. https://doi.org/10.1016/j.joitmc.2025.100474.
Ko, H.-K., Park, G., Jeon, H., Jo, J., Kim, J. & Seo, J. (2023). Large-scale text-to-image generation models for visual artists’ creative works. Proceedings of the 28th International Conference on Intelligent User Interfaces. https://doi.org/10.1145/3581641.3584078.
Kolawole, O. (2025). Guardrails for artificial general intelligence: A strategic foresight approach to ethical AI governance. World Journal of Advanced Research and Reviews, 28(1), 743–758. https://doi.org/10.30574/wjarr.2025.28.1.3505
Kretschmer, M., Margoni, T., & Oruç, P. (2024). Copyright law and the lifecycle of machine learning models. IIC - International Review of Intellectual Property and Competition Law, 55, 110–138. https://doi.org/10.1007/s40319-023-01419-3
Manteghi, M. (2024). Can text and data mining exceptions and synthetic data training mitigate copyright-related concerns in generative AI? Law, Innovation and Technology, 16(2), 663–686. https://doi.org/10.1080/17579961.2024.2392928
Maurizio, B., Khan, B., Arnaudo, A., Riccardo, R., Ricolfi, M., Vetrò, A. & Riccardo, C. (2025). The development of generative artificial intelligence from a copyright perspective. Unito.it, pp.1–435. https://doi.org/10.2814/3893780.
Mazzi, F. (2024). Authorship in artificial intelligence-generated works: Exploring originality in text prompts and artificial intelligence outputs through philosophical foundations of copyright and collage protection. The Journal of World Intellectual Property, 27(3), 410–427. https://doi.org/10.1111/jwip.12310
Mehta, P. (2026). Generative AI and creative life: Creator perspectives on model training and the limits of copyright protection. Utexas.edu. https://repositories.lib.utexas.edu/items/19a77323-8f4e-4c5a-8a1a-c994852a8b3c.
Murati, E. (2023). Methodology. 13–19. https://doi.org/10.1007/978-3-031-46731-8_2
Niyompatama, M. J., & Lapatoura, I. (2025). Generative AI in fashion design creation: A copyright analysis of AI-assisted designs. Journal of Intellectual Property Law & Practice, 20(10), 654–666. https://doi.org/10.1093/jiplp/jpaf045
Pawar, H.B., Neelanshu Neelanshu, Bhat, G.M., Srikanth, A. & Muragundi, P.M. (2025). Challenges of using artificial intelligence (natural language processing) tools for regulatory documentation. Journal of Applied Pharmaceutical Science. https://doi.org/10.7324/japs.2026.267918.
Prasad, A.N. (2024). Regulatory compliance and risk management. Apress eBooks, pp.485–624. https://doi.org/10.1007/979-8-8688-1023-7_8.
Quintais, J. P. (2025). Generative AI, copyright and the AI Act. Computer Law & Security Review, 56, 106107. https://doi.org/10.1016/j.clsr.2025.106107
Ramos-Zaga, F.A. (2025). Reconceptualizing human authorship in the age of generative AI: A normative framework for copyright thresholds. Laws, 14(6), 84. https://doi.org/10.3390/laws14060084.
Rosati, E. (2025). Infringing AI: Liability for AI-generated outputs under international, EU, and UK copyright law. European Journal of Risk Regulation, 16(2), 603–627. https://doi.org/10.1017/err.2024.72
Rough, E. & Sutherland, N. (2024). Artificial intelligence: A reading list. House of Commons Library. https://commonslibrary.parliament.uk/research-briefings/cbp-10003/.
Saw, C. L., & Tan, B. Z. Y. (2025). Unpacking copyright infringement issues in the GenAI development lifecycle and a peek into the future. Computer Law & Security Review, 58, 106163. https://doi.org/10.1016/j.clsr.2025.106163
Schubert, K. D., & Barrett, D. (2024). Data Governance, Privacy, and Ethics. Technology, work and globalization, pp.87–110. https://doi.org/10.1007/978-3-031-51063-2_5.
Senftleben, M. (2023). Generative AI and author remuneration. IIC - International Review of Intellectual Property and Competition Law, 54, 1535–1560. https://doi.org/10.1007/s40319-023-01399-4
Senftleben, M. (2026). Text and data mining, generative AI, and the copyright three-step test. IIC - International Review of Intellectual Property and Competition Law, 57, 67–107. https://doi.org/10.1007/s40319-026-01680-2
Sharma, D., & Kumar, N. (2026). Balancing AI innovation and copyright protection: A comparative analysis of the United Kingdom’s proposed text and data mining framework and the European Union’s TDM exception. International Journal of Global Innovations and Modern Research, 1(1), 103–112. https://doi.org/10.66304/ijgimr.2026.v1i1.22.
Sharma, Y. (2026). Comparative governance and regulatory approaches towards generative artificial intelligence. Anusandhanvallari, pp.2614–2624. https://psvmkendra.com/index.php/journal/article/view/2199.
Taeihagh, A. (2025). Governance of generative AI. Policy and Society, 44(1), 1–22. https://doi.org/10.1093/polsoc/puaf001
Tyagi, K. (2024). Copyright, text & data mining and the innovation dimension of generative AI. Journal of Intellectual Property Law & Practice, 19(7), 557–570. https://doi.org/10.1093/jiplp/jpae028
Ulnicane, I. (2025). Governance fix? Power and politics in controversies about governing generative AI. Policy and Society, 44(1), 70–84. https://doi.org/10.1093/polsoc/puae022
Vranopoulos, G., Clarke, N. & Atkinson, S. (2023). Big data confidentiality: An approach toward corporate compliance using a rule-based system. Big data. https://doi.org/10.1089/big.2022.0201.
Watiktinnakorn, C., Seesai, J., & Kerdvibulvech, C. (2023). Blurring the lines: How AI is redefining artistic ownership and copyright. Discover Artificial Intelligence, 3(1). https://doi.org/10.1007/s44163-023-00088-y.
Wu, H. (2024). Copyright protection during the training stage of generative AI: Industry-oriented U.S. law, rights-oriented EU law, and fair remuneration rights for generative AI training under the UN’s international governance regime for AI. Computer Law & Security Review, 55, 106056. https://doi.org/10.1016/j.clsr.2024.106056
Xu, Z. (2025). Intellectual property rights issues in AI-Generated content. Journal of Computer Technology and Electronic Research, 2(7). https://doi.org/10.70767/jcter.v2i7.741.
Zhao, D. (2026). AI-Driven Digital Marketing Strategies in business launch and early growth: An empirical study on UK startups. ResearchGate. https://www.researchgate.net/publication/399453620_AI-Driven_Digital_Marketing_Strategies_in_Business_Launch_and_Early_Growth_An_Empirical_Study_on_UK_Startups.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Srija Yerram
This work is licensed under a Creative Commons Attribution 4.0 International License.
