International Journal of Science, Engineering and TechnologyAuthors: Vignesh P. Rajan
Abstract: The exponential growth of enterprise data, including structured, semi-structured, and unstructured formats, has created significant challenges for effective information governance. Organizations must ensure data quality, security, accessibility, and compliance with regulatory frameworks such as GDPR, HIPAA, and PCI DSS. Traditional governance approaches, including manual tagging, rule-based classification, and static metadata management, often fail to scale, leading to inefficiencies, inconsistencies, and compliance risks. AI-powered data classification offers a transformative solution by leveraging machine learning, deep learning, and natural language processing to automatically categorize data based on content, context, and sensitivity. These systems improve accuracy, reduce human error, enable real-time classification, and support comprehensive data lifecycle management. This review examines the impact of AI-driven data classification on information governance, focusing on conceptual frameworks, classification techniques, integration with governance policies, applications across enterprises, challenges, and emerging trends. Evidence from industry case studies demonstrates that AI-powered classification enhances regulatory compliance, strengthens data security, improves operational efficiency, and supports informed decision-making. The review also addresses limitations, including model biases, data privacy concerns, integration with legacy systems, and the need for explainable AI. Finally, future directions are discussed, emphasizing context-aware classification, cloud-native architectures, autonomous governance, and ethical considerations for responsible AI deployment. Overall, AI-powered data classification represents a critical enabler of modern information governance, allowing organizations to manage vast and complex data landscapes with accuracy, efficiency, and strategic oversight.
