Responsible Generative AI: Governance Challenges and Solutions in Enterprise Data Clouds

Zifan Chen; Ying Wang; Xiuyuan Zhao

doi:10.54097/02teq773

Authors

Zifan Chen
Ying Wang
Xiuyuan Zhao

DOI:

https://doi.org/10.54097/02teq773

Keywords:

Generative AI, Governance, Enterprise Data Cloud, Responsible AI, Data Ethics, Compliance

Abstract

The emergence of a faster generative artificial intelligence (AI) has changed the way businesses use data, automate choices and innovate within cloud-based ecosystems. But as companies start deploy large language models and other generative systems to the data clouds of their enterprises, governance, accountability, and ethical oversight issues have also become more complicated. This paper discusses the governance issues related to generative AI implementation in data infrastructure of the enterprise and suggests a model of responsible governance that balances innovation and compliance, security, and ethics. Based on the recent scholarship and policy changes, the research outlines the key pillars of governance - transparency, accountability, data stewardship, fairness, and regulatory compliance and evaluates their adoption in the multi-cloud settings. The article, by synthesizing the concepts and providing illustrative examples in the industry, emphasizes the necessity to have combined mechanisms of data and AI governance that could help resolve such problems like data provenance, mitigation of bias, and monitoring of the lifecycle. Finally, the paper concludes that responsible governance does not hinder enterprise innovation, but is a strategic facilitator of a credible and sustainable application of AI.

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References

[1] Areddy, P. R. (2025). AI-driven data governance in cloud computing: Ensuring compliance and ethical AI practices. International Journal of Computer Engineering and Technology (IJCET, 16(3), 580–588. https://doi.org/10.34218/IJCET_16_03_035.

[2] Cugurullo, F., & Xu, Y. (2025). When AIs become oracles: Generative AI, anticipatory urban governance, and the future of cities. Policy and Society. https://doi.org/10.1093/polsoc/puae025.

[3] Eisenberg, I., Gamboa, L., & Sherman, E. (2025). The unified control framework: Establishing a common foundation for enterprise AI governance, risk management and regulatory compliance. arXiv. https://doi.org/10.48550/arXiv.2503.05937.

[4] Ettinger, A. (2025). Enterprise architecture as a dynamic capability for scalable and sustainable generative AI adoption. arXiv. https://doi.org/10.48550/arXiv.2505.06326.

[5] Folorunso, A., Adewa, A., Babalola, O., & Nwatu, C. E. (2024). A governance framework model for cloud computing: Role of AI, security, compliance, and management. World Journal of Advanced Research and Reviews, 24(2), 1969–1982. https://doi.org/10.30574/wjarr.2024.24.2.3513.

[6] Ghosh, A., & Lakshmi, D. (2023). Dual governance: The intersection of centralized regulation and crowdsourced safety mechanisms for generative AI. arXiv. https://doi.org/10.48550/arXiv.2308.04448.

[7] Janssen, M. (2025). Responsible governance of generative AI: Conceptualizing AI governance as a complex adaptive system. Policy and Society. https://doi.org/10.1093/polsoc/puae040.

[8] Judge, B., Nitzberg, M., & Russell, S. (2025). When code isn’t law: Rethinking regulation for artificial intelligence. Policy and Society. https://doi.org/10.1093/polsoc/puae020.

[9] Khanal, S., Zhang, H., & Taeihagh, A. (2025). Why and how is the power of Big Tech increasing in the policy process? Policy and Society. https://doi.org/10.1093/polsoc/puae012.

[10] Kyi, L., Mahuli, A., Silberman, M. S., Binns, R., Zhao, J., & Biega, A. J. (2025). Governance of generative AI in creative work: Consent, credit, compensation, and beyond. arXiv. https://doi.org/10.48550/arXiv.2501.11457.

[11] Li, Y., Taeihagh, A., de Jong, M., & Klinke, A. (2021). Toward a commonly shared public policy perspective on AI governance. Policy & Society. https://doi.org/10.1080/14494035.2021.1929728.

[12] Luna, J., et al. (2024). A cross-regional comparative study of generative AI governance. AIES 2024 Proceedings. https://ojs.aaai.org/index.php/AIES/article/view/31692.

[13] Luna, J., Tan, I., Xie, X., & Jiang, L. (2024). Navigating governance paradigms: A cross-regional comparative study of generative AI governance processes & principles. arXiv. https://doi.org/10.48550/arXiv.2408.16771.

[14] Mäntymäki, M., Ahokangas, P., & Oksanen, A. (2022). Defining organizational AI governance. AI and Ethics. https://doi.org/10.1007/s43681-022-00143.

[15] Nakajima, R. (2025). The generative artificial intelligence governance paradox: Driving innovation while challenging global corporate oversight in multinational firms. In M. Pazarskis, A. Kostyuk, V. Santolamazza, & P. Capuano (Eds.), Corporate governance: Scholarly research and practice (pp. 91–95). Virtus Interpress. https://doi.org/10.22495/cgsrapp17.

[16] Napoli, P. M. (2025). Social media’s lessons for generative AI governance. Media, Culture & Society. https://doi.org/10.1080/1369118X.2025.2513668.

[17] Schneider, J. (2023). Artificial intelligence governance for businesses. Journal of Information Systems & Policy. https://doi.org/10.1080/10580530.2022.2085825.

[18] Schneider, J., & colleagues. (2024). Integrating data governance and AI governance: A unified approach. ACM Governance Journal. https://doi.org/10.1145/3628454.3628461.

[19] Schneider, J., et al. (2024). Governance of generative artificial intelligence for companies. arXiv. https://doi.org/10.48550/arXiv.2403.08802.

[20] Taeihagh, A. (2025). Governance of generative AI. Policy and Society. https://doi.org/10.1093/polsoc/puaf001.

[21] Ulnicane, I. (2025). Governance fix? Power and politics in controversies about governing generative AI. Policy and Society. https://doi.org/10.1093/polsoc/puae022.

[22] Zaidan, E., & Ibrahim, I. A. (2024). AI governance in a complex and rapidly changing regulatory landscape: A global perspective. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-024-03560.

[23] Zhang, H., Khanal, S., & Taeihagh, A. (2024). Public-private powerplays in the generative AI era: Balancing big tech regulation amidst the global AI race. Digital Government: Research and Practice. https://doi.org/10.1145/3664824.