An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics

Abstract

The rapid digital transformation of financial services and critical infrastructure systems has increased reliance on cloud-based platforms while simultaneously amplifying security risks and operational vulnerabilities. This paper presents an AI-based risk-aware cloud security framework designed to secure financial workflows and enable smart wastewater analytics through real-time predictive intelligence. The proposed framework integrates machine learning–driven risk assessment, continuous monitoring, and adaptive security controls to detect threats, anomalies, and compliance violations across distributed cloud environments. By leveraging real-time data streams from financial transactions and wastewater monitoring systems, the framework enables proactive risk mitigation and informed decision-making. Advanced analytics models support fraud detection, operational optimization, and early identification of system failures while ensuring data confidentiality and integrity. The architecture emphasizes scalability, resilience, and regulatory compliance, making it suitable for highly regulated domains. Experimental evaluation demonstrates improved threat detection accuracy, reduced response latency, and enhanced reliability compared to traditional rule-based security approaches. The results highlight the effectiveness of AI-driven, risk-aware cloud security in supporting secure financial operations and intelligent, data-driven wastewater management.