(untitled) (International Journal of Advanced Research in Computer Science & Technology(IJARCST), 2026)
Modern corporate landscapes generate enormous volumes of high-velocity, heterogeneous data spread across distinct, isolated operational environments. To derive actionable business insights while maintaining corporate agility, organizations are rapidly adopting artificial intelligence platforms to power their decision-making frameworks. However, standard enterprise deployments face two critical engineering challenges: the "black-box" nature of advanced deep learning algorithms, which violates transparency mandates, and the security vulnerabilities associated with centralizing sensitive data into a single infrastructure provider. This paper presents a comprehensive framework for architecting intelligent enterprise intelligence using Explainable AI (XAI) integrated within a highly secure multi-cloud computing environment. By incorporating mathematical feature-attribution methods and local surrogate models directly into the analytical layer, the framework provides transparent, human-interpretable explanations for automated business outcomes. Concurrently, to ensure data security, multi-region compliance, and zero single-point-of-failure exposure, the architecture distributes workloads dynamically across multiple public and private cloud providers. This infrastructure uses secure cryptographic protocols and automated multi-cloud mesh routing to guarantee data sovereignty. Empirical performance evaluations demonstrate that this integrated blueprint improves regulatory compliance validation rates by 45%, reduces single-vendor cloud dependency risks to zero, and achieves sub-second latency targets for globally distributed enterprise queries.