Enhancing Behavioral Analytics with Zero Trust in Cloud: A Comparative Analysis

The digital age of technology has paved the way for advanced methods like cloud computing that have completely revolutionized the way traditional systems in organizations work. This advancement in cloud computing provides an effective way to handle large amounts of data, applications, and services. As with any critical system, the security of the cloud is of utmost importance. Researchers used the zero trust model working on the principle “never trust, always verify” to provide security to cloud computing. This model continuously verifies and monitors the interactions of users and devices to provide a robust security framework. Behavioral analytics, which has become important due to its ability to detect anomalies, has been integrated with the zero-trust model to enhance detection and provide efficient security measures against threats. However, a variety of limitations still needs to be addressed to further enhance behavioral analytics with zero trust. To address this, in this paper, we performed a study of zero trust model architecture and its steps of implementation. We also explored different zero trust mechanisms and their integration with behavioral analytics. We also evaluated the existing work by drawing a comparison between them based on their strengths and weaknesses. We identified the gaps present in the existing mechanisms and then based on that we gave future directions that will be beneficial for future researchers. Overall, our work provides an effective insight into existing problems and provides a direction for future progress for the researchers.