AI-Powered Financial Federated Learning Architecture for Healthcare Cybersecurity on AWS Cloud
DOI:
https://doi.org/10.15662/IJARCST.2023.0605010Keywords:
AI, Federated Learning, Financial Systems, Healthcare Cybersecurity, AWS Cloud, Risk Management, Data PrivacyAbstract
The growing adoption of cloud computing in healthcare and financial sectors has significantly enhanced operational efficiency and analytics-driven decision-making. However, it also exposes sensitive data to evolving cybersecurity threats, including data breaches, insider attacks, and fraud. This paper proposes an AI-Powered Financial Federated Learning Architecture for Healthcare Cybersecurity on AWS Cloud, a framework designed to provide secure, scalable, and intelligent protection for sensitive data. The framework leverages federated learning to enable collaborative AI model training across distributed financial and healthcare datasets without sharing raw data, ensuring privacy preservation. It integrates cloud-native services on AWS to support real-time threat detection, anomaly identification, and proactive cyber risk mitigation. Security mechanisms, including encryption, access control, and compliance monitoring, ensure alignment with regulatory standards such as HIPAA, PCI-DSS, and GDPR. Experimental evaluation demonstrates enhanced detection accuracy, reduced response latency, and robust protection against emerging cyber threats. This architecture provides a secure and intelligent solution for managing cybersecurity challenges in multi-institutional cloud environments.References
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