END-TO-END DATA PROTECTION: CYBERSECURITY STRATEGIES IN BIG DATA ENGINEERING
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.