Applied Sciences
| Open Access | A Multidisciplinary Analytical Model For Ai-Powered Regulatory Automation And Performance Optimization In Cloud-Integrated Critical Infrastructure Systems
Dr. Pierre Lambert , Department of Smart Computing Monaco Institute of Technology MonacoAbstract
The increasing complexity of critical infrastructure systems in financial and healthcare ecosystems has intensified the demand for intelligent automation mechanisms capable of ensuring regulatory compliance, operational efficiency, and data integrity. Traditional compliance systems are largely rule-based, static, and unable to adapt to rapidly evolving regulatory frameworks and distributed cloud-native architectures. This research proposes a multidisciplinary analytical model for AI-powered regulatory automation and performance optimization in cloud-integrated environments. The study synthesizes concepts from artificial intelligence, cloud computing, regulatory analytics, and distributed systems engineering to design a unified compliance optimization framework.
The proposed model integrates machine learning-driven compliance monitoring, cloud-native orchestration mechanisms, and data lineage tracking systems to ensure transparency and auditability across heterogeneous infrastructures. Foundational cloud computing principles highlight the transformation of IT resources into scalable utility-based services (Buyya et al., 2009), which forms the basis for regulatory automation at scale. The study further examines how metadata management, secure access control, and AI-driven anomaly detection contribute to improving decision-making accuracy and reducing operational overhead.
A structured analytical framework is developed through systematic synthesis of existing literature in healthcare informatics, financial risk governance, and cloud systems engineering. The findings indicate that AI-enhanced compliance systems significantly improve regulatory responsiveness, reduce latency in audit processes, and enhance predictive risk detection capabilities. However, challenges such as model drift, interoperability constraints, and regulatory ambiguity persist.
This research contributes a comprehensive theoretical and applied model that bridges gaps between regulatory science and cloud-based AI systems, offering a scalable architecture for next-generation critical infrastructure governance.
Keywords
Artificial Intelligence, Cloud Computing, Regulatory Automation, Critical Infrastructure
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