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Advancing Intelligent Automation through Secure Cloud Engineering with Intelligent Decision Support for Enterprise Resilience

Abstract

Modern enterprises navigate a volatile, uncertain, and highly interconnected digital ecosystem where operational continuity hinges on rapid, data-driven adaptability. This paper introduces an architectural blueprint for advancing intelligent automation by synthesizing secure cloud engineering paradigms with Intelligent Decision Support Systems (IDSS) to foster robust enterprise resilience. Traditional automated systems, while highly efficient at executing repetitive tasks, frequently falter when confronted with unpredictable operational anomalies, supply chain disruptions, or sophisticated cyber-physical threats. By embedding cognitive decision models and prescriptive analytics directly within secure, cloud-native infrastructures, organizations can transform rigid automation loops into dynamic, context-aware frameworks. This research investigates the strategic orchestration of microservices, zero-trust cloud network security, and machine learning algorithms to enable autonomous event triage and real-time risk mitigation. Furthermore, we evaluate how intelligent decision support bridges the operational gap between tactical machinery and strategic human oversight during systemic crises. The resulting framework guarantees that enterprise data pipelines remain secure, scalable, and highly available under continuous operational duress. Ultimately, this study demonstrates that unifying secure cloud engineering with cognitive decision support provides the structural agility required to withstand digital disruptions, optimize asset utilization, and ensure long-term corporate survivability.

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