SECURE CLOUD DATA STORAGE WITH A ZERO TRUST SECURITY FOUNDATIONAL DEEP LEARNING ALGORITHM

Considering the current state of the digital world, the protection of data stored in the cloud has emerged as an essential concern for commercial enterprises. Classical security measures that are focused on perimeters are no longer adequate to safeguard sensitive data from cyber threats that are becoming increasingly sophisticated. The Zero Trust Security model necessitates stringent authentication and authorization procedures, as well as constant monitoring of all authorization and access attempts. This model is based on the assumption that threats might originate from both inside and outside the network. Integration of deep learning algorithms with Zero Trust Security principles is the innovative solution that is proposed in this research for the purpose of safeguarding data storage that is hosted in the cloud. Through the utilization of deep learning techniques, such as Autoencoders, Long Short-Term Memory Networks (LSTMs), and Generative Adversarial Networks (GANs), the primary objective is to improve the detection of anomalies and risks within cloud systems. It is possible for these algorithms to automatically alter security settings and detect potential breaches in real time by evaluating user behavior, access patterns, and network traffic. The incorporation of deep learning algorithms into a Zero Trust framework provides a security system that is more intelligent and adaptable, allowing it to grow in response to new threats as they emerge. Using a combination of the powers of deep learning's pattern recognition capabilities and the proactive and comprehensive security mechanisms of Zero Trust Security, this strategy intends to provide increased security for cloud-based data storage. This will be accomplished by integrating the two approaches.