Generative AI for Enterprise Trust: A Governance-Aligned Framework for Safe and Transparent Automation at Global Scale
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P124Keywords:
Generative AI governance, enterprise AI safety, controlled generation, human-in-the-loop, AI transparency, policy-aligned AI systemsAbstract
The rapid adoption of Generative Artificial Intelligence (GenAI) across global enterprises has fundamentally transformed business automation, decision-making, and knowledge work. While these technologies offer unprecedented productivity gains, they simultaneously introduce critical risks related to data privacy, model opacity, regulatory compliance, ethical misuse, and operational reliability. Existing AI governance approaches often fail to scale effectively or align with enterprise trust requirements across jurisdictions. This paper proposes a Governance-Aligned Generative AI Framework (GAGAF) designed to ensure safe, transparent, auditable, and compliant GenAI deployment at global enterprise scale. The framework integrates governance principles directly into the AI lifecycle, embedding risk management, explainability, human oversight, and regulatory alignment into system architecture rather than treating them as post-deployment controls. The study synthesizes existing literature on AI governance, trustworthiness, and enterprise automation, identifying gaps in current methodologies. A layered architecture is introduced, comprising policy orchestration, model governance, operational controls, and continuous assurance mechanisms. The methodology employs a design-science research approach, validated through simulated enterprise deployment scenarios across regulated industries including finance, healthcare, and manufacturing. Results demonstrate that governance-embedded GenAI systems significantly reduce compliance violations, improve explainability metrics, and enhance organizational trust without degrading system performance. The findings suggest that trust-centric AI governance is not only feasible but essential for sustainable GenAI adoption at scale. This work contributes a scalable reference architecture for enterprises seeking to operationalize GenAI responsibly while meeting global regulatory and ethical expectations
References
[1] European Commission High-Level Expert Group on Artificial Intelligence. (2019). Ethics guidelines for trustworthy AI. European Commission.
[2] IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. (2019). Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems (1st ed.). IEEE.
[3] Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
[4] Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
[5] Raji, I. D., Smart, A., White, R. N., et al. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAccT), 33–44.
[6] Amodei, D., Olah, C., Steinhardt, J., et al. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
[7] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 610–623.
[8] Weidinger, L., Mellor, J., Rauh, M., et al. (2022). Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359.
[9] Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
[10] Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21.
[11] European Union. (2024). Regulation (EU) 2024/— Artificial Intelligence Act (AI Act). Official Journal of the European Union.
[12] ISO/IEC. (2023). ISO/IEC 42001: Artificial intelligence — Management system. International Organization for Standardization.
[13] Veale, M., & Borgesius, F. Z. (2021). Demystifying the draft EU Artificial Intelligence Act. Computer Law Review International, 22(4), 97–112.
[14] Kroll, J. A., Huey, J., Barocas, S., et al. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–705.
[15] Goyal, Mahesh Kumar, et al. "Leveraging Generative AI for Database Migration: A Comprehensive Approach for Heterogeneous Migrations." Available at SSRN 5222550 (2025).
[16] Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An overview of AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 26(4), 2141–2168.
