Zero Trust Security Framework for Federated and Centralized Enterprise Data Architectures: A Comparative Analysis of AI-Enhanced Database Integration Models

Abstract

This article presents a comprehensive analysis of Zero Trust Architecture implementation in enterprise data environments, examining the comparative security effectiveness of federated versus centralized data models within AI-enhanced database integration systems. The article explores the evolution from traditional perimeter-based security models to Zero Trust paradigms, addressing the complex challenges of hybrid cloud environments and multi-source data integration. Through systematic review of theoretical frameworks, the research investigates core Zero Trust principles including continuous verification, granular identity management, and layered security architectures specifically designed for database-centric systems. The architectural analysis reveals distinct security implications between distributed and unified data storage approaches, evaluating access control mechanisms, data sovereignty considerations, and performance trade-offs across different deployment models. The integration of artificial intelligence technologies with Zero Trust frameworks demonstrates significant potential for real-time threat detection and automated response capabilities, while specialized encryption protocols for AI-driven data pipelines ensure comprehensive data protection throughout machine learning workflows. Regulatory compliance implementation within GDPR and HIPAA frameworks requires sophisticated policy engines capable of automated enforcement across complex data processing environments. The research synthesizes security effectiveness across architectural models, providing strategic recommendations for enterprise implementation and identifying future research directions including quantum-resistant cryptography, edge computing integration, and privacy-preserving computation techniques that will shape the evolution of Zero Trust data architectures.