Hybrid cloud architectures have emerged as a foundational paradigm for modern enterprise data systems, enabling seamless interoperability between on-premise infrastructure and public cloud environments. However, existing synchronization mechanisms remain limited in their adaptability, particularly when handling heterogeneous NoSQL data models, dynamic workloads, and bidirectional consistency requirements. This paper proposes an AI-native, configuration-driven framework for hybrid cloud synchronization that enables intelligent, policy-aware, and bidirectional data integration between on-premise systems and NoSQL cloud databases. The proposed model leverages adaptive configuration orchestration, trust-aware synchronization layers, and predictive optimization techniques to ensure data consistency, security, and operational efficiency across distributed environments. Prior research highlights persistent challenges in cloud trust establishment, identity management, and secure data exchange (Abawajy, 2011; Bhardwaj and Kumar, 2011). Building upon these foundations, this study integrates AI-driven decision modules to optimize synchronization routing, reduce latency, and enhance fault tolerance in hybrid deployments. The framework also incorporates insights from hybrid cloud security models, energy-aware virtual machine management, and federated identity systems to ensure scalability and resilience. A conceptual evaluation demonstrates improved synchronization efficiency, reduced data conflict rates, and enhanced configuration flexibility compared to traditional ETL-based approaches. The study contributes a novel architectural perspective that bridges AI-driven automation with hybrid cloud data engineering, offering a scalable solution for enterprise-grade NoSQL integration in distributed cloud ecosystems.

Hybrid Cloud, AI-Native Systems, NoSQL Databases, Data Synchronization, Configuration-Driven Architecture, Bidirectional Data Flow, Cloud Computing Security, Federated Cloud, Data Integration, Intelligent Cloud Systems

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