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Abstract
As data volume, velocity, and variety continue to expand exponentially, do the risks associated with securing sensitive information within big data ecosystems. Cybersecurity is no longer just a network concern—it is now a fundamental pillar of modern data engineering. This paper presents a comprehensive exploration of end-to-end data protection strategies tailored for big data pipelines. We identify and dissect the security challenges that span the data lifecycle, from ingestion to consumption, particularly within distributed and cloud-native environments. This paper introduces CySecDataFlow, a modular, scalable framework that integrates key principles of encryption, identity management, data masking, auditing, and compliance into data engineering practices. The discussion further extends into advanced areas such as zero-trust security models, AI-driven threat detection, and future-ready cryptographic techniques.
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