Intelligent Enterprise Computing through Hybrid Cloud Predictive Analytics and Cyber Resilience Framework
DOI:
https://doi.org/10.15680/IJMRSETM.2025.0106012Keywords:
Hybrid Cloud, Predictive Analytics, Cybersecurity, Digital Transformation, Artificial Intelligence, Machine Learning, Zero Trust Security, Edge Computing, Cloud Computing, Data Security, Enterprise ArchitectureAbstract
Modern enterprises are rapidly adopting hybrid cloud architectures to support scalable computing, real-time analytics, and secure digital transformation. However, increasing cyber threats, distributed workloads, and data-intensive applications demand intelligent systems capable of predictive cybersecurity and adaptive resource management. This paper proposes an Intelligent Hybrid Cloud Architecture (IHCA) that integrates predictive analytics, artificial intelligence (AI), and cybersecurity automation to enhance enterprise resilience and operational efficiency. The architecture combines private and public cloud environments with edge computing nodes to ensure low-latency processing and secure data orchestration. Machine learning models are embedded within the cloud infrastructure to analyze behavioral patterns, detect anomalies, and predict potential cyberattacks before they occur. Additionally, the framework supports automated workload balancing, intelligent threat response, and continuous compliance monitoring. Digital transformation is achieved through seamless integration of legacy systems with cloud-native microservices, enabling agility and scalability. The proposed system also incorporates zero-trust security principles and data encryption mechanisms to protect sensitive enterprise assets. By leveraging predictive intelligence and hybrid cloud flexibility, organizations can significantly improve decision-making, reduce security risks, and optimize IT resource utilization. The study highlights the effectiveness of combining AI-driven cybersecurity with hybrid cloud ecosystems to support next-generation enterprise digital transformation strategies
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