Enhancing Security In Big Data Environments: A Framework For Real-Time Threat Detection And Mitigation

Abstract

The advancement in the big data environments frontline has made it possible for organizations to get immense benefits from the environments. Thus, this type of growth has also brought a number of security issues in terms of protecting information and the integrity of the data. Based on the analysis of pros and cons of existing approaches, this paper introduces the conceptual model of real-time threat detection and protection of big data. It extends the usage of the machine learning algorithms, the detection of anomalies, and the analysis of the threats in real-time. The use of Distributed Computation and Big data analysis ensures that the framework can analyze large data at this speed and volumetric nature to ensure that vulnerabilities are identified, and potential attacks are addressed before they aggravate. The proposed approach also includes methods for adaptation of the model with respect to new threats, what ensures further immunization against new forms of cyber attack. Also, there the towering framework for data governance model to ensure access controls and data protection laws compliance. The incorporation of these elements within scalable architecture framework offers complete solution for big data security. Example evidence and real-world outcomes indicate the functionality of the proposed framework in the decrease in the detection time of threats and the impact on the data. From this research, it emerges that the concept of real-time security is important, especially in the big data environment and presents a practical model that organizations can adopt. The results highlighted the need for real-time surveillance and quick response as well as the necessity to learn from experiences in defending large-scale data structures.