AI-Driven Data Automated Auditing and Governance Frameworks for Enterprise Data Engineering

Abstract

With the challenges of large-scale data evolving and enterprises battling with the complexities of the big-data ecosystem, there is growing urgency in the end-to-end intelligent and automated data auditing and governance systems. The conventional governance arrangements have been relatively inelastic, rigid, and unaware, which makes them inadequate to deal with changing and nonhomogeneous data spaces. This review is an insight into how artificial intelligence (AI) overhauls the prospect of next-gen data governance and auditing in enterprise data engineering. AI-enabled systems deliver ongoing quality, lineage, policy enforcement, and regulatory compliance by using machine learning, natural language processing, and knowledge graph technology to examine metadata and data lineages and the policies associated with them. These technologies improve decision making, cut down any human error, and provide an opportunity to predict anomalies in data and risks of accessing information. The article addresses the use of predictive auditing, automation of the interpretation of policies, and context-aware access control enabled by AI. It also lists some of the critical implementation issues as biased data, the explainability of the models, and the intricacy of involving the system. Moreover, the review details the role that federated and reinforcement learning play in the context of safe and flexible governance of distributed systems. With the introduction of AI and data governance together, the future of enterprise data engineering is bringing the missing pieces of transparency, compliance, and operational resilience. The paper summarizes the existing studies and provides a guideline for implementing AI-driven governance architectures to achieve sustainable, compliant, and efficient data management behaviors.