AI-Driven continuous compliance and threat intelligence model for adaptive GRC in complex digital ecosystems

The rapid evolution of digital ecosystems—characterized by multi-cloud infrastructures, IoT proliferation, and distributed data flows—has fundamentally altered the governance, risk, and compliance (GRC) landscape. Traditional GRC frameworks, rooted in periodic audits and reactive controls, are increasingly inadequate for addressing the scale, speed, and sophistication of modern cyber threats. This review paper examines the emergence of AI-driven continuous compliance and threat intelligence models as adaptive solutions for managing GRC in complex digital environments. It synthesizes existing literature on regulatory mapping, continuous auditing, and real-time threat intelligence integration, while identifying key limitations of siloed, manual approaches. The study highlights how artificial intelligence and machine learning can enable proactive risk identification, predictive analytics, and automated remediation, transforming GRC into a continuous and intelligent function. Furthermore, the paper explores technical methodologies such as natural language processing for regulatory interpretation, anomaly detection algorithms for compliance monitoring, and predictive modeling for risk forecasting. By analyzing current advancements, challenges, and research gaps, this review proposes a conceptual framework that positions AI as a catalyst for adaptive, resilient, and future-ready GRC architectures. The findings underscore the critical need for intelligent, real-time governance models to ensure organizational sustainability in the face of regulatory volatility and cyber risk escalation.  Keywords: Continuous Compliance, Threat Intelligence, Adaptive GRC, Artificial Intelligence in Governance, Predictive Risk Analysis, Automated Remediation.