Secure Federated AI and Cloud Data Engineering Systems for Scalable Enterprise Intelligence and Governance Platforms

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Published: 2024-11-12
DOI: https://doi.org/10.15662/IJRPETM.2024.0706028
Keywords:
Federated AI, Data Governance, Secure Machine Learning, Distributed Systems, Differential Privacy, Data Pipelines, Enterprise Intelligence, Zero Trust Security, Scalable AI Architecture

Main Article Content

Thomas Dohmke
Senior Software Engineer, GitHub, Germany

Abstract

Secure federated AI integrated with cloud data engineering is emerging as a critical architecture for modern enterprise intelligence systems. As organizations increasingly rely on distributed data ecosystems spanning multiple clouds, edge devices, and organizational boundaries, traditional centralized data processing approaches face limitations in scalability, privacy, and regulatory compliance. Federated AI enables machine learning across decentralized datasets without requiring raw data movement, thereby enhancing privacy preservation and reducing data exposure risks. When combined with cloud-native data engineering pipelines, organizations can achieve scalable ingestion, transformation, orchestration, and governance of heterogeneous data sources in real time. This paper explores the design principles, architectural patterns, and governance frameworks necessary to build secure federated AI systems in cloud environments. It further examines encryption techniques, secure multiparty computation, differential privacy, and policy-driven access control mechanisms that ensure compliance with global data protection regulations. Additionally, it highlights enterprise use cases such as financial fraud detection, healthcare analytics, and supply chain optimization. The study also discusses challenges including latency, model drift, interoperability, and cross-domain governance. Finally, it proposes a unified framework for scalable enterprise intelligence platforms that balance security, performance, and regulatory compliance in distributed AI ecosystems.

Article Details

Issue

Vol. 7 No. 6 (2024): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Secure Federated AI and Cloud Data Engineering Systems for Scalable Enterprise Intelligence and Governance Platforms. (2024). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11688-11694. https://doi.org/10.15662/IJRPETM.2024.0706028

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