The rapid expansion of cloud-native computing and real-time analytics has fundamentally changed how contemporary enterprises process and leverage data. Organizations now depend on streaming data pipelines to enable fraud detection, cybersecurity monitoring, Internet of Things (IoT) analytics, operational intelligence, customer personalization, and artificial intelligence (AI)-driven decision-making. Technologies such as Apache Kafka, Apache Flink, Kubernetes, and cloud-native data platforms facilitate scalable, low-latency processing of large data volumes. Nevertheless, the distributed architecture of these systems introduces substantial cybersecurity challenges, including insecure application programming interfaces (APIs), ransomware attacks, insider threats, software supply chain vulnerabilities, and data leakage.

This paper analyzes the security challenges inherent in realtime big data pipelines within cloud-native enterprise environments and proposes a comprehensive security framework that integrates zero-trust principles, encryption, identity and access management, governance, observability, container security, and AI-driven threat detection. The study further presents statistical trends, security risk assessments, compliance considerations, and architectural models to illustrate evolving industry priorities and best practices. The aim of this research is to offer practical and scalable strategies for securing next-generation real-time streaming infrastructures while preserving operational agility and resilience.

Big Data Security, Cloud-Native Architecture, Real-Time Analytics, Kubernetes Security, Apache Kafka, Zero Trust, Streaming Security, Cybersecurity, Data Governance, AI Security