Micro segmentation And Zero Trust Architecture For Adaptive Security Enforcement In Cloud Networks

Abstract

The rising use of cloud computing has also provided exposure to the enterprise networks to advanced cyber threats, especially lateral movement attacks and insiders abuse, which the traditional perimeter-based security model does not adequately address. Zero Trust Architecture (ZTA) has become an exciting concept because it eradicates the unwritten trust in the paradigm by imposing a never-ending identity validation and the least-privilege access. Nevertheless, the use of Zero Trust without internal traffic control on a fine level is prone to the spread of east-west attacks. In an attempt to overcome this drawback, this paper presents an adaptive Zero Trust-enabled architecture of microsegmentation that can be implemented in the cloud network involving identity-based access control and network isolation (workload) remedies. The offered methodology uses the continuous risk assessment of the data based on identity attributes, contextual data, and real-time telemetry to dynamically implement micro-perimeters and limit the unauthorized communication between workloads. A policy decision, risk assessment, and adaptive segmentation is formulated mathematically to have a scalable and rigorous implementation. Based on experimental assessment on a cloud testbed, the suggested framework would significantly decrease the rate of lateral movement success, enhance attack containment, and decrease the rate of policy violation with a low level of enforcement latency. These findings confirm the usefulness and efficiency of implementing Zero Trust and adaptive microsegmentation in securing the modern cloud environments.