Unified Cross-Domain Framework for Adaptive Zero-Trust and Risk-Aware Access Control Across Cloud, Big Data, Storage, and Network Environments

The fragmentation of modern IT infrastructures has exposed a critical gap in how security is conceptualized and executed across heterogeneous environments. Cloud platforms, large-scale storage systems, distributed big data clusters, and interconnected network fabrics each bring unique operational models and threat landscapes. Yet security approaches remain compartmentalized, lacking the coordinated structure to manage risks that span these boundaries. This paper proposes a unified cross-domain adaptive security framework integrating zero-trust principles, Usage Control (UCON) models, and reinforcement learning techniques such as Deep Q-Network optimization to enable continuous trust reassessment across cloud, storage, and network environments. The framework emphasizes dynamic, risk-aware access control that adapts permissions throughout the lifecycle of resource interactions, limiting opportunities for adversaries to exploit static or siloed protections. By embedding continuous trust evaluation, behavioral monitoring, and automated policy adaptation into a cohesive architecture, organizations can strengthen resilience against sophisticated multi-domain threats while maintaining operational continuity.