Architecting Secure Big Data Environments: Risk Management Strategies for Hadoop, Spark, and Cloud Deployments

The rapid growth of big data has introduced significant challenges in ensuring data security, access control, and compliance in cloud-native architectures. This study explores a conceptual framework for securing big data environments by integrating robust data governance, encryption mechanisms, real-time monitoring, risk assessment, and incident response strategies. By leveraging system intelligence and automation, organizations can enhance data protection, reduce vulnerabilities, and improve scalability. The framework highlights the interdependencies between key security components, emphasizing the role of continuous improvement in mitigating emerging threats. Furthermore, the study examines performance trends in cloud platforms, such as Google Cloud Platform (GCP), Microsoft Azure, and Hadoop, comparing their ingestion speed, query performance, and scalability. The findings indicate that system intelligence enhances data processing efficiency and security compliance while optimizing resource allocation. As cyber threats continue to evolve, adopting a structured security framework ensures data integrity and regulatory adherence. This research contributes to the growing discourse on big data security, offering a roadmap for organizations to strengthen their cybersecurity posture in large-scale cloud deployments. Future work will explore AI-driven security models for real-time threat detection.