Compliance-as-Code 2.0: Orchestrating Regulatory Operations with Agentic AI

Aman Sardana (), Swaminathan Sethuraman () and Priya Dharshini Kalyanasundaram ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 5, issue 1, 546-563

Abstract: Modern enterprises face escalating challenges in maintaining regulatory compliance amid fragmented standards, evolving cyberthreats, and costly manual audits. This paper introduces Compliance-as-Code 2.0, a novel framework leveraging agentic AI to automate and orchestrate regulatory operations at scale. By embedding autonomous AI agents within a dynamic policy engine, the system translates regulations (e.g., GDPR, PCI-DSS, CCPA) into executable code, enabling real-time monitoring, self-healing controls, and predictive compliance workflows. The framework integrates natural language processing (NLP) to interpret regulatory texts, reinforcement learning to optimize control deployment, and blockchain for auditable policy versioning. In a 12-month trial with a multinational bank, Compliance-as-Code 2.0 reduced manual audit efforts by 74%, cut compliance-related downtime by 63%, and detected 98% of policy violations preemptively. Case studies in healthcare and fintech demonstrated a 55% faster response to new regulations (e.g., EU AI Act) and a 40% reduction in third-party compliance costs. However, challenges persist in balancing explainability with AI autonomy, particularly in high-stakes environments like financial reporting. The study concludes with a roadmap for deploying agentic compliance systems, emphasizing cross-industry collaboration to address ethical AI governance and interoperability standards. By transforming compliance from a reactive cost center to a strategic asset, this framework redefines regulatory agility in the digital age.

Keywords: Compliance Automation; Agentic AI; Regulatory Operations; Dynamic Policy Engines; GDPR; PCI-DSS; Predictive Compliance; Self-Healing Systems (search for similar items in EconPapers)
Date: 2024
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