Authors: Amelia R. Hill, Edward J. Perry, Allison M. Ward, Chaitanya Srinivas, Aneesha Raj
Abstract: The increasing volume of sensitive data processed across modern enterprise ecosystems has intensified the need for secure data architecture models that ensure confidentiality, integrity, avail-ability, and regulatory compliance. Organizations operating in sectors such as healthcare, finance, government, telecommunications, and critical infrastructure manage large amounts of personally identifiable information (PII), protected health information (PHI), financial records, and other sensitive data that are subject to stringent privacy and security regulations. The rapid adoption of cloud computing, distributed systems, big data platforms, artificial intelligence, and hybrid enter-prise environments has further increased the complexity of protecting sensitive information throughout its lifecycle. This study presents a systematic evidence mapping of secure data archi-tecture models for sensitive data environments to provide a comprehensive overview of existing research, architectural frameworks, security mechanisms, implementation approaches, and emerg-ing trends. The evidence mapping systematically classifies published literature based on architec-tural models, data protection techniques, privacy-preserving technologies, encryption mecha-nisms, identity and access management, zero-trust architectures, secure cloud infrastructures, data governance, compliance frameworks, and risk management strategies. The analysis identifies the most widely adopted security principles, including defense-in-depth, least privilege, secure-by-design, data minimization, tokenization, anonymization, pseudonymization, encryption, continu-ous monitoring, and policy-driven access control. The findings indicate that effective secure data architectures integrate technical, organizational, and governance controls through layered security models that combine strong authentication, access control, metadata management, data classifica-tion, secure storage, data lineage, auditing, and continuous compliance monitoring. Furthermore, the study highlights the growing adoption of artificial intelligence, machine learning, confidential computing, privacy-enhancing technologies, and automated security orchestration to strengthen enterprise data protection and improve operational resilience. Despite these advancements, several challenges remain, including evolving cyber threats, heterogeneous enterprise environments, regulatory complexity, interoperability limitations, scalability concerns, and balancing data acces-sibility with privacy requirements. The evidence mapping also identifies promising future re-search directions in autonomous security architectures, explainable artificial intelligence for cyber-security, zero-trust data ecosystems, quantum-resistant cryptography, privacy-preserving analyt-ics, and intelligent compliance automation. Overall, this study provides researchers, practitioners, and policymakers with a comprehensive understanding of secure data architecture models and offers valuable insights for designing resilient, scalable, and regulation-compliant enterprise archi-tectures that protect sensitive information while supporting secure digital transformation and sustainable organizational growth.
DOI: http://doi.org/10.5281/zenodo.21944322
