Enhancing data security in cloud-Based Analytics Platforms

Abstract

Cloud-based analytics platforms have revolutionized data processing and decision-making by providing scalable, flexible, and cost-effective computing resources. However, the inherent distributed nature and multi-tenancy of cloud environments introduce significant security challenges. This manuscript examines advanced techniques for enhancing data security within these platforms. We explore current vulnerabilities, review contemporary literature on encryption, access control, and risk management strategies, and propose a comprehensive methodology integrating advanced encryption standards, robust authentication protocols, and continuous monitoring systems. Through simulation and case study analyses, our findings demonstrate that layered security frameworks and proactive threat intelligence significantly mitigate risks while maintaining system performance. The implications of our work extend to enterprises seeking to safeguard sensitive analytics data without sacrificing the inherent advantages of cloud computing. Future research directions are also outlined, including the potential integration of artificial intelligence in threat detection and the need for standardized security protocols across heterogeneous cloud environments.