Zero-Trust Security Automation Using Knowledge Graphs and Predictive Risk Intelligence in Multi-Cloud Enterprise Networks

Abstract

The increasing adoption of multi-cloud computing has introduced significant cybersecurity challenges associated with distributed identities, complex access permissions, interconnected applications, and continuously changing network environments. Traditional perimeter-based security mechanisms are insufficient to protect modern enterprise infrastructures against sophisticated cyber threats, unauthorized access, privilege escalation, and data breaches. This research proposes a zero-trust security automation framework that integrates knowledge graphs and predictive risk intelligence to strengthen access control, threat detection, and security policy enforcement across multi-cloud enterprise networks. The proposed approach uses knowledge graphs to represent relationships among users, devices, applications, cloud resources, access permissions, and security events, enabling contextual analysis of enterprise security conditions. Predictive risk intelligence employs machine learning techniques to identify abnormal behavioral patterns, estimate potential security risks, and support proactive decision-making. Automated zero-trust mechanisms continuously evaluate access requests using identity verification, device posture, resource sensitivity, behavioral indicators, and contextual risk scores. The framework aims to improve security visibility, minimize excessive privileges, enhance threat identification, and enable adaptive policy enforcement across heterogeneous cloud platforms. The proposed research also examines the importance of explainability, scalability, privacy preservation, and human supervision in automated security operations. This integrated approach provides a foundation for developing intelligent, context-aware, and proactive enterprise security architectures capable of protecting dynamic multi-cloud environments.