An Intelligent AI-Based Predictive Cybersecurity Architecture for Financial Workflows and Wastewater Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605008Keywords:
AI-based cybersecurity, predictive analytics, financial workflow security, wastewater analytics, anomaly detection, cloud-native security, real-time monitoringAbstract
The convergence of cloud computing, artificial intelligence, and critical infrastructure has introduced significant cybersecurity challenges for modern financial workflows and wastewater analytics platforms. This paper proposes an intelligent, AI-based predictive cybersecurity architecture designed to secure financial operations and smart wastewater systems through proactive threat detection and real-time analytics. The architecture employs machine learning and behavioral analysis techniques to anticipate cyber threats, detect anomalies, and evaluate risk across distributed cloud environments. Continuous monitoring of financial transactions and wastewater sensor data enables early identification of fraud, cyber intrusions, and operational disruptions. Adaptive security controls, automated incident response mechanisms, and continuous compliance monitoring are integrated to ensure data confidentiality, integrity, and availability. The cloud-native design of the framework supports scalability, resilience, and seamless integration with enterprise systems. Experimental evaluation demonstrates higher threat prediction accuracy, reduced detection latency, and improved system reliability compared to traditional reactive security approaches. The results confirm the effectiveness of AI-driven predictive cybersecurity in safeguarding financial workflows and enabling intelligent, data-driven wastewater analytics.
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