The Future of AI in Governance: Building Self-Adapting GRC Systems

Naresh Kumar Methuku

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 1, 1110-1119

Abstract: The potential of artificial intelligence to revolutionize the creation of self-adapting Governance, Risk, and Compliance (GRC) systems is explored in this article. It discusses how organizations are transitioning from traditional, manual GRC processes to sophisticated AI-driven solutions that can autonomously adapt to evolving regulatory landscapes. The article illustrates how reinforcement learning, adaptive algorithms, and neural networks reshape GRC practices by examining current implementation challenges, technological foundations, and future opportunities. It also discusses critical implementation factors across technological, regulatory, and organizational aspects while highlighting real-world applications in intelligent policymaking, predictive risk management, and ongoing compliance monitoring. The article addresses future developments in GRC technology, such as enhanced ecosystem creation, advanced analytics, and integration capabilities. Analyzing prospects and challenges provides valuable insights for companies implementing self-adapting GRC systems while maintaining robust governance frameworks.

Keywords: Self-Adapting GRC Systems; AI-Powered Compliance; Regulatory Technology Innovation; Automated Risk Management; Integrated Governance Frameworks (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112114
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT251112114 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT251112114/CSEIT251112114 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i1:id:771

DOI: 10.32628/CSEIT251112114

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().