AI-Enabled Cloud Architecture for Healthcare Real-Time Analytics and Cybersecurity in Financial Systems
DOI:
https://doi.org/10.15662/IJARCST.2024.0704010Keywords:
AI-enabled cloud architecture, Healthcare real-time analytics, Cybersecurity, Financial systems, Machine learning, Predictive analyticsAbstract
The increasing digitization of healthcare and financial services has significantly enhanced operational efficiency but has also introduced heightened cybersecurity risks and challenges in managing real-time data. This paper proposes an AI-enabled cloud architecture designed to support healthcare real-time analytics while ensuring robust cybersecurity for financial systems. The proposed framework integrates advanced machine learning models with cloud-native principles, leveraging scalable APIs, microservices, and secure data pipelines to process and analyze high-volume, heterogeneous datasets in real time. AI-driven anomaly detection and predictive analytics enhance the system’s ability to identify cyber threats, detect fraud, and support informed decision-making in both healthcare and financial domains. Security-by-design principles, including encryption, access control, and continuous monitoring, are embedded to ensure compliance with regulatory standards such as HIPAA and financial industry requirements. Experimental evaluation demonstrates that the architecture achieves low-latency processing, high throughput, and accurate threat detection, outperforming traditional batch-based and non-API systems. The findings indicate that AI-enabled cloud architectures provide a scalable, secure, and efficient platform for integrating real-time analytics and cybersecurity across healthcare and financial infrastructures.
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