Leveraging Generative AI for Adaptive Compliance Workflows in Enterprise Platforms
Abstract
The integration of Generative Artificial Intelligence (GenAI) into enterprise compliance operations allows businesses to create flexible, compliant, real-time workflows that can respond to changing regulations with ease [2]. Traditional compliance practices are primarily based on human interpretation of policies, manual drafting of compliance documents, and the subsequent re-configuration of these documents and workflows — all of which cannot sustain a compliant business in an environment where regulatory changes occur continuously [3]. This paper presents the Generative Adaptive Compliance Orchestration Framework (GACOF) [10], which is integrated into the ServiceNow Governance, Risk, and Compliance (GRC) solution [5]. This framework utilizes large language models (LLMs) [7][8], natural language processing (NLP) [7] and workflow orchestration to automatically analyze and interpret regulatory updates and generate compliance policy drafts based upon those updates, along with changing compliance workflows as regulations change (almost) in real-time. A financial services example demonstrates the efficacy of the GACOF framework in reducing policy update cycles by 40%, manual policy drafting workload by 93% and regulatory interpretation accuracy rate by 95%. The research presents a new model of AI-based compliance orchestration that combines GenAI with retrieval-augmented generation (RAG) [9] and human-in-the-loop validation, which provides enterprise businesses with a scalable and auditable AI-based solution for compliance globally. Based upon these findings, GenAI will act as a multiplier of compliance professionals, not as a substitute for them, but rather as a technology that will allow for compliance ecosystems to be proactive and to continually self-adapt to meet new compliance requirements [10].
Fields of Study & Subjects
Business Process Modeling and Analysis
Explainable Artificial Intelligence (XAI)
Robotic Process Automation Applications
Publication details
- Journal / source
- International Journal for Research in Applied Science and Engineering Technology
- Publisher
- International Journal for Research in Applied Science and Engineering Technology
- Volume
- 14
- Issue
- 1
- Pages
- 1717–1722
- Publication date
- 31 January 2026