Policy Driven and Zero Trust AI Architectures for Secure Data Lakes and Fraud Detection and Migration and Real Time Enterprise Intelligence

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Published: 2024-09-12
DOI: https://doi.org/10.15662/IJRPETM.2024.0705010
Keywords:
Zero Trust Architecture, Policy-Driven Governance, Secure Data Lakes, AI Security, Fraud Detection, Enterprise Intelligence, Data Migration, Micro-Segmentation, Real-Time Analytics, Data Governance, Cybersecurity Architecture

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Oliver Matthias Felsenbruch
Independent Researcher, Berlin, Germany

Abstract

The rapid expansion of enterprise data ecosystems has intensified the need for secure, intelligent, and resilient architectures. Traditional perimeter-based security models are insufficient in protecting modern data lakes, particularly when supporting AI-driven fraud detection, large-scale data migration, and real-time enterprise intelligence. This paper explores the integration of policy-driven governance frameworks and Zero Trust Architecture (ZTA) principles within AI-enabled data lake environments. A policy-driven approach ensures data access, processing, and analytics are governed by dynamic, automated policies aligned with compliance, security, and operational requirements. Zero Trust enhances this by enforcing continuous verification, least-privilege access, and micro-segmentation across all users, devices, and workloads.

 


The study examines architectural models, security enforcement mechanisms, AI-powered fraud detection pipelines, and secure migration strategies. It further proposes a research methodology for implementing and evaluating such systems within enterprise environments. The findings demonstrate that combining policy-driven governance with Zero Trust AI significantly enhances data protection, reduces fraud risks, supports secure cloud migration, and enables real-time decision intelligence. However, implementation complexity, operational costs, and integration challenges remain key concerns. The paper concludes by outlining advantages, disadvantages, and future research directions in secure AI-driven enterprise data ecosystems.

Article Details

Issue

Vol. 7 No. 5 (2024): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Policy Driven and Zero Trust AI Architectures for Secure Data Lakes and Fraud Detection and Migration and Real Time Enterprise Intelligence. (2024). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(5), 11203-11210. https://doi.org/10.15662/IJRPETM.2024.0705010

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