Secure GovCloud: Enhancing Security, Scalability, and Reliability in E-Governance Systems through Advanced Cloud Security Mechanisms

Abstract

In the contemporary digital era, e-governance systems are pivotal for enhancing transparency, efficiency, and citizen engagement, but face significant security challenges related to data confidentiality, integrity, and regulatory compliance. This research introduces the SecureGovCloud model, designed to address these challenges through advanced security mechanisms. The primary objective is to enhance the security, scalability, and reliability of e-governance systems. Using a mixed-methods approach, the model integrates context-aware access control (CAAC), quantum-resistant and homomorphic encryption, and an AI-based anomaly detection system (AI-ADS). These features were implemented and rigorously tested in a virtualized cloud environment, demonstrating that SecureGovCloud significantly outperforms traditional RBAC and ABAC models. Key findings include lower response times (e.g., 4.5 seconds vs. 5.0 seconds under a load of 10,000 requests), higher throughput (e.g., 340 transactions/second vs. 310 transactions/second at peak load), and more efficient resource utilization (e.g., 85% CPU usage vs. 90%). Additionally, the model exhibits robust security, detecting and preventing a higher percentage of cyber-attacks, and maintaining a low false positive rate. Compliance adherence is also enhanced, with fewer violations and quicker resolution times. In conclusion, SecureGovCloud sets a new benchmark for secure and efficient digital governance, offering significant improvements in performance and security. The model's scalability and compliance management make it a viable solution for modern e-governance systems, fostering greater public trust and enabling seamless service delivery. Future research should focus on optimizing the model under extreme conditions and exploring the integration of emerging technologies like blockchain to further strengthen the security framework.