AI-Driven Zero Trust For Healthcare And Enterprise Networks: A Unified Framework For Intelligent Security Architecture

Abstract

The growth of interconnected digital systems in healthcare and enterprise settings has established new security risks that demand intelligent and adaptive protection systems that have never been applied before. This converged framework is a synthesis of artificial intelligence and Zero Trust security principles to fulfill the requirements of a changing cyber threat by using behavioral analytics, anomaly detection, and micro-segmentation approaches. The suggested architecture includes four layers that are linked to each other: network telemetry collection, machine learning-based intelligent processing, dynamic policy implementation, and healthcare-specific augmentation modules. Machine learning algorithms such as isolation forests, deep neural networks, and ensemble classifiers can be used to identify more complex attack patterns with low false positive rates due to contextual awareness and behavioral profiling. The implementation strategies can deal with both the technical and operational issues with the help of phased implementation, identity-based access control, and management of network segments with the help of software definition. Experimental validation shows significant enhancement in threat detection accuracy, incident containment effectiveness, and operational efficiency without causing a disruption in clinical workflow and regulatory compliance. The framework has managed to be secure without compromising user experience and is available enough to be used in critical systems without causing perceptible performance degradation. Augmentation that is specific to healthcare incorporates clinical context into security decision-making, minimizing false alarms in cases of emergencies and ensuring a high degree of security of sensitive patient information. This intersection of artificial intelligence and Zero Trust architecture signifies a necessary change in cybersecurity approach to preventive measures that organizations must use to manage sensitive data in the context of a distributed and heterogeneous network.