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Cyber-Resilient Enterprise Data Governance Using Explainable AI and Multi-Cloud Security Architecture

Abstract

The rapid adoption of cloud computing, artificial intelligence, distributed applications, and multi-cloud infrastructures has increased the complexity of enterprise data governance and cybersecurity. Organizations increasingly store and process sensitive information across multiple public, private, and hybrid cloud environments, creating challenges related to data visibility, access control, regulatory compliance, threat detection, and incident response. This paper proposes a cyber-resilient enterprise data governance framework that integrates Explainable Artificial Intelligence (XAI) with a multi-cloud security architecture. The proposed approach combines centralized governance policies, distributed security controls, identity-centric access management, continuous monitoring, data classification, encryption, and AI-driven anomaly detection. Explainable AI is incorporated to improve the transparency and interpretability of security decisions, enabling security teams to understand why particular activities are classified as legitimate, suspicious, or malicious. The methodology emphasizes policy consistency across heterogeneous cloud platforms while preserving flexibility for organization-specific security requirements. The framework also incorporates zero-trust principles, automated risk assessment, security orchestration, and resilient backup and recovery mechanisms. By integrating governance and cybersecurity capabilities, organizations can improve visibility, reduce attack surfaces, strengthen regulatory compliance, and accelerate incident response. The proposed architecture provides a practical foundation for organizations seeking trustworthy, adaptive, and resilient enterprise data management across complex multi-cloud environments.

 

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