Ensuring Data Compliance: The role of AI and ML in securing Enterprise Networks
Seshagirirao Lekkala
Abstract
This work describes data compliance complexities and the preventive architecture principles required to identify and thwart associated information breaches. The fundamental quest in the elaboration of data compliance mechanisms is not only to identify those fires but to circumvent or contain the wildfires before and after they erupt. Machine learning (ML) and artificial intelligence (AI) technologies that augment cybersecurity technologies can play a major role here by learning, simulating, and analyzing adverse information scenario potentials. Various architecture patterns emphasizing preventive cautionary methods and practices related to data compliance at facilities entrusted with sensitive information are previewed using data sensitivity, risk, severity, continuity/integrity, and examination/audibility compliance prerequisite patterns.