Advanced cloud security framework based on zero trust architecture and adaptive deep learning for next-generation systems

Abstract

Static rule-based models and cloud access security brokers (CASBs) — traditional cloud security frameworks— can no longer effectively mitigate modern and evolving cyber threats. Two such examples include signature-based detection methods which lack real-time versatility and are ineffective against advanced persistent threats or zero-day threats. In this paper, we introduce an adaptive zero trust framework (AZTF) based on the integration of zero trust architecture (ZTA) and adaptive deep learning (ADL) approach to dynamically evaluate threats and risks being targeted on cloud environments. It continually monitors access attempts using DL models for real-time anomaly detection. Nine synthetic datasets were generated and used in the experiment in two security domains: network traffic and access pattern. The proposed system reached 96% detection accuracy, 52% improvements in response time, and 12% resource consumption optimization compared to traditional ZTA-based security models. The results highlight the power of using a combination of continuous authentication with artificial intelligence (AI)-powered dynamic security policy application to strengthen the resilience of cloud security. Future research will focus on federated learning integration, multi-cloud security applications, and explainable AI for increased transparency of models.