Adaptive Security Model for Cloud Platforms Based on Information Security and Cryptographic Protocols

Abstract

Purpose: This paper proposes an adaptive security model designed for cloud platforms, integrating information security principles and cryptographic protocols to address evolving cybersecurity threats. The model ensures dynamic security control adjustments based on real-time risk assessments to protect enterprise applications and business operations. Methodology: The proposed model employs continuous monitoring, intelligent threat detection, and automated responses to proactively mitigate risks. It integrates cryptographic techniques such as AES, RSA, and elliptic-curve cryptography to secure data transmission, storage, and access control. Additionally, machine learning-driven anomaly detection and behavioral analytics dynamically refine security policies. Findings: The model enhances cloud security resilience against data breaches, unauthorized access, and service disruptions. By leveraging automated security orchestration, it ensures scalability, resilience, and operational efficiency while minimizing system overhead. The experimental implementation confirms its effectiveness in mitigating threats while maintaining high system performance and availability. Unique Contribution to Theory, Practice, and Policy (Recommendations): This research contributes to cloud security advancements by presenting a comprehensive, adaptable security framework that addresses both known and emerging attack vectors. It ensures compliance with industry regulations such as GDPR, HIPAA, and SOC 2, providing organizations with a structured approach to meeting evolving cybersecurity mandates. Future research should explore enhanced AI-driven security orchestration, quantum-resistant cryptographic protocols, and cross-cloud security interoperability to further fortify cloud infrastructures.