AI-Powered Cloud Cybersecurity Architecture for Risk Prediction and Threat Mitigation in Healthcare and Finance
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Abstract
The rapid digitalization of healthcare and financial services through cloud computing has significantly improved scalability, accessibility, and data-driven decision-making. However, this transformation has also increased exposure to sophisticated cyber threats, including data breaches, ransomware attacks, fraud, and insider threats. Traditional rule-based security mechanisms are insufficient to address the dynamic and large-scale nature of these risks. To address these challenges, this paper proposes an AI-Powered Cloud Cybersecurity Architecture for Risk Prediction and Threat Mitigation in Healthcare and Finance. The proposed architecture integrates cloud-native security services with artificial intelligence and machine learning techniques to enable proactive risk prediction, real-time threat detection, and automated response. Machine learning models analyze heterogeneous data sources such as network traffic, system logs, user behavior, and transactional records to identify anomalies and predict potential cyber risks. The architecture incorporates threat intelligence feeds, continuous monitoring, and adaptive security controls to enhance resilience against evolving attacks. In addition, compliance requirements specific to healthcare and financial domains, including HIPAA, PCI-DSS, and GDPR, are supported through policy-driven governance and audit-ready analytics. Experimental evaluation and domain-specific use cases demonstrate improved detection accuracy, reduced response time, and enhanced visibility into cybersecurity risks across multi-cloud environments. The proposed solution provides a scalable, intelligent, and secure framework for strengthening cyber defense in modern healthcare and financial ecosystems.
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