Enhanced Zero Trust Model for Cloud-Native Microservices Using Gradient Boosting Algorithm

The rapid rise of cloud-native microservices has given birth to a new set of problems and security risks that the old defense mechanisms at the perimeter have been unable to manage. It explores the Zero Trust security concept for cloud environment microservices, which are empowered by a machine learning model. Through the use of the Gradient Boosting Algorithm (GBA), the primary means of the project can make intelligent decisions for access requests by interim classification of them with such parameters as user behaviour, resource sensitivity, request origin, and past data interaction patterns as context factors. GBA leads to higher precision and real-time authentication and reconfirmation of the access legitimacy status by processing vast datasets more effective manner, avoiding noise, and generally enhancing decision-making. In real cases, it's a method that uses a set of base predictors to design a strong predictive system for detecting infrequent activities and making the right decisions as to what is unwanted and what the demand is, with higher accuracy. Besides, the employment of this supervised learning model in inter-service communication equally confirms the uninterrupted security principles of Zero Trusted, while at the same time, it doesn't affect the proposed system in terms of performance. The Performance evaluation of the model is being computed, and the security problems have been reduced is as the experiment results are showing that, and at the same time that the data flexibility and the deployment capacity needs in the recent cloud-native architectures are preserved. This undertaking goes one step further in the area by employing Zero Trust's proactive scheme alongside the predictive power of GBA to give a complete solution to microservices' data security in the cloud.