The rapid advancement of distributed computing and cloud storage has significantly transformed data processing, storage, and security paradigms. However, the increasing complexity of data-driven systems demands robust and explainable models to ensure transparency, accountability, and trust. This paper proposes an aggregated Explainable Artificial Intelligence (XAI) framework that integrates real-time intelligent data security, distributed computing resilience, and explainable cloud storage mechanisms. The framework leverages interpretable machine learning models, secure federated learning, and explainable cryptographic protocols to provide comprehensive visibility into decision-making processes. Experimental results demonstrate improved transparency, real-time anomaly detection, and enhanced compliance with regulatory frameworks while minimizing computational overhead. The proposed model enables stakeholders to understand, trust, and validate AI-driven security mechanisms in heterogeneous cloud and distributed environments.
Subrahmanyam et al. (Mon,) studied this question.