Artificial intelligence in corporate legal risk management: A governance and accountability framework
DOI:
https://doi.org/10.62452/3av1ph39Keywords:
Artificial intelligence, corporate legal risk, legal risk management, corporate governance, regulatory compliance, legal technology, predictive analytics, ethical governanceAbstract
The acceleration of the regulatory requirement, globalization of operations and digital transformation has increased the corporate vulnerability to legal risk. The current legal risk management methods which are mostly reactive and manual fail to handle the scale and complexity of the modern legal and compliance issues. Corporations are in turn moving towards artificial intelligence (AI) more and more as a method of improving legal risk identification, evaluation, and reduction. The paper explores the ways of using AI to balance the control of corporate legal risk and governance, accountability, and ethical responsibility. Using a qualitative and conceptual research of the existing literature, the study examines the major types of corporate legal risk such as regulatory compliance, contractual risk, litigation exposure, governance failures, and data protection issues. The paper examines AI applications within the corporate legal functions, including natural language processing, machine learning, and predictive analytics, and puts into the limelight, how these applications can be used to facilitate proactive and predictive risk management. Meanwhile, the paper critically considers the issues related to the use of AI such as algorithmic bias, transparency, threats to privacy, and legal accountability ambiguity. To resolve the challenges a conceptual framework is suggested, which incorporates the AI capabilities in the corporate level of legal risk management in a regulation-focused and ethically sound framework. The article adds to the literature because it offers a comprehensive view of AI-sensitive law risk management and offers useful data that corporations should consider in terms of finding the balance between innovation and legal accountability.
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References
Alotaibi, E. M. (2023). Risk Assessment Using Predictive Analytics. International Journal of Professional Business Review, 8(5), e01723. https://doi.org/10.26668/businessreview/2023.v8i5.1723
Armour, J., & Sako, M. (2020). AI-enabled business models in legal services: From traditional law firms to next-generation law companies. Journal of Professions and Organization, 7(1), 27–46. https://doi.org/10.1093/jpo/joaa001
Chiu, I. H.-Y., & Lim, E. W. K. (2021). Managing corporations’ risk in adopting artificial intelligence: A corporate responsibility paradigm. Washington University Global Studies Law Review, 20(2), 347–389. https://journals.library.wustl.edu/globalstudies/article/id/8690/
Getman, A. P., Yaroshenko, O. M., Shapoval, R. V., Prokopiev, R. Y., & Demura, M. I. (2023). The impact of artificial intelligence on legal decision-making. International Comparative Jurisprudence, 9(2). https://doi.org/10.13165/j.icj.2023.12.001
Hickman, E., & Petrin, M. (2021). Trustworthy AI and corporate governance: The EU’s ethics guidelines for trustworthy artificial intelligence from a company law perspective. European Business Organization Law Review, 22(4), 593–625. https://doi.org/10.1007/s40804-021-00224-0
Kalkan, G. (2024). The impact of artificial intelligence on corporate governance. Journal of Corporate Finance Research, 18(2), 17–25. https://doi.org/10.17323/j.jcfr.2073-0438.18.2.2024.17-25
Kappel, G. (2024). Risks of artificial intelligence-based decision support and decision-making systems in executive-level decision-making in companies: A literature review. Pécs Journal of International and European Law, 2024(2), 14–46. https://doi.org/10.15170/PJIEL.2024.2.3
Mayienga, B. A., Onwuzulike, O. C., Oyeyipo, I., Ayodeji, D. C., Nwaozomudoh, M. O., Attipoe, V., & Ahmadu, J. (2024). A conceptual model for global risk management, compliance, and financial governance in multinational corporations. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 1351–1363. https://doi.org/10.62225/2583049X.2024.4.6.3916
Mbonigaba, C., Vinayakan, K., Hakizimana, L., & Mbonigaba, C. (2025). The future of AI-driven legal compliance: How artificial intelligence is enhancing corporate governance and regulatory adherence. International Journal of Current Research and Modern Education, 10(1), 26–36. https://doi.org/10.5281/zenodo.15059151
Parimi, S. K., & Yallavula, R. (2023). Enterprise risk intelligence: Machine learning models for predicting compliance, fraud, and operational failures. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 173–181. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P117
Scherer, M. U. (2016). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harvard Journal of Law & Technology, 29(2), 353–400. https://ssrn.com/abstract=2609777
Schmitz, A., & Wimmer, M. A. (2025). Enhancing public procurement through GRC management: Navigating the evolving EU regulatory landscape. Conference on Digital Government Research, 26. https://doi.org/10.59490/dgo.2025.978
Shahriar, S., Allana, S., Hazratifard, S. M., & Dara, R. (2023). A survey of privacy risks and mitigation strategies in the artificial intelligence life cycle. IEEE Access, 11, 61829–61854. https://doi.org/10.1109/ACCESS.2023.3287195
Solanke, B., Onita, F. B., Ochulor, O. J., & Iriogbe, H. O. (2024). The impact of artificial intelligence on regulatory compliance in the oil and gas industry. International Journal of Science and Technology Research Archive, 7(1), 61–72. https://doi.org/10.53771/ijstra.2024.7.1.0058
Veith, C., Bandlow, M., Harnisch, M., Wenzler, H., Hartung, M., & Hartung, D. (2016). How legal technology will change the business of law. The Boston Consulting Group & Bucerius Law School. https://ink.library.smu.edu.sg/sol_research/4618
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Copyright (c) 2026 Vikas Sharma, Nidhi Arya (Autor/a)

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