A Cloud-Native AI Analytics Framework for Managing Cybersecurity Risks in Healthcare and Financial Systems
DOI:
https://doi.org/10.15662/IJEETR.2024.0605007Keywords:
Cloud-Native Security, Artificial Intelligence, Cybersecurity Risk Management, Healthcare Systems, financial systems, Machine Learning Analytics, Real-Time Threat DetectionAbstract
The rapid digital transformation of healthcare and financial systems has significantly increased exposure to complex and evolving cybersecurity threats. Traditional security mechanisms often lack the scalability, adaptability, and real-time intelligence required to protect sensitive data in cloud-centric environments. This paper proposes a cloud-native AI analytics framework designed to proactively manage and mitigate cybersecurity risks across healthcare and financial domains. The framework integrates machine learning–driven threat detection, real-time data ingestion, and automated risk assessment using scalable microservices deployed on cloud infrastructure. Advanced analytics enable early identification of anomalies, fraud patterns, and intrusion attempts, while compliance-aware security controls support regulatory requirements such as HIPAA, GDPR, and financial governance standards. By leveraging cloud elasticity and AI-driven insights, the proposed framework enhances resilience, reduces response time, and improves overall security posture. Experimental analysis demonstrates the framework’s effectiveness in achieving accurate threat detection, scalable performance, and adaptive risk management in dynamic, data-intensive environments.
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