Secure and Resilient AI-Driven Architectures for Next-Generation Cyber-Physical and Cloud Enterprise Systems
DOI:
https://doi.org/10.15662/IJARCST.2026.0902006Keywords:
AI security, cyber-physical systems, cloud computing, resilience, zero trust architecture, federated learning, anomaly detection, blockchain security, edge computing, adversarial attacksAbstract
The rapid convergence of artificial intelligence (AI), cloud computing, and cyber-physical systems (CPS) is transforming modern enterprise infrastructures. These systems, which integrate computational intelligence with physical processes, are increasingly deployed in critical domains such as smart manufacturing, healthcare, transportation, and energy. However, their growing complexity and interconnectivity introduce significant security vulnerabilities and resilience challenges. This paper explores the design and implementation of secure and resilient AI-driven architectures tailored for next-generation cyber-physical and cloud enterprise systems. It emphasizes the integration of adaptive AI models, zero-trust security frameworks, distributed cloud-edge infrastructures, and real-time threat detection mechanisms. The proposed architecture leverages machine learning for anomaly detection, blockchain for data integrity, and federated learning for privacy preservation. Furthermore, resilience is enhanced through self-healing systems, redundancy strategies, and fault-tolerant design principles. The study also examines existing limitations and emerging threats, including adversarial AI attacks and data poisoning. By synthesizing current research and proposing a robust architectural framework, this work aims to guide enterprises in building secure, scalable, and resilient systems capable of operating in dynamic and hostile environments. The findings contribute to advancing trustworthy AI-enabled cyber-physical ecosystems.References
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