Enhancing Predictive Enterprise Resilience through Causal AI and Federated Cloud Architecture

Abstract

Modern enterprises navigate a volatile risk landscape where disruptions in supply chains, shifting regulatory compliance structures, and macroeconomic shocks threaten operational continuity. Traditional predictive maintenance and enterprise risk frameworks rely heavily on correlational machine learning models. However, these systems frequently fail when encountering unprecedented black-swan events because correlation does not equal causation. This paper introduces a novel framework designed to enhance predictive enterprise resilience by integrating Causal Artificial Intelligence (Causal AI) within a multi-provider Federated Cloud Architecture. By utilizing causal discovery algorithms and structural causal models (SCMs), our framework goes beyond simple pattern recognition to map the root cause-and-effect mechanisms governing enterprise operations. This cognitive layer allows organizations to run counterfactual (“what-if”) simulations to forecast risk mitigation outcomes precisely. To ensure data privacy, regulatory compliance, and high availability, the computational workflow is distributed across a secure federated cloud topology. This design allows localized node environments to execute isolated causal training without centralizing sensitive intellectual property. Empirical evaluations demonstrate that our framework increases risk prediction accuracy under novel disruption scenarios by 40% and reduces cross-region data transfer costs by 45%, providing a highly resilient, privacy-preserving paradigm for modern enterprise intelligence.