Data Governance and Compliance in Pega Applications: Strategies, Best Practices, and Regulatory Requirements

Abstract

Ensuring data governance and compliance within enterprise applications is a critical
challenge, especially in highly regulated industries. Pega applications provide robust capabilities
for automating compliance processes, but traditional rule-based approaches often struggle to keep
up with evolving regulations and large-scale data environments. This paper explores how Artificial
Intelligence (AI) can enhance data governance in Pega applications by leveraging machine
learning for anomaly detection, compliance monitoring, and regulatory adherence. We implement
an AI-driven compliance framework and evaluate its performance against traditional rule-based
systems. The results demonstrate that AI-based approaches significantly improve anomaly
detection accuracy, compliance adherence, and governance efficiency. These findings highlight
the potential of AI in optimizing regulatory compliance and reducing manual intervention in Pega
environments.