Abstract :

Cloud Computing Has Revolutionized The Healthcare Sector By Enabling Scalable Infrastructure, Efficient Data Sharing, And Costeffective Storage Solutions. However, The Rapid Growth In The Volume Of Sensitive Patient Data Stored In Cloud Environments Has Introduced Significant Security Challenges, Including Cyberattacks, Data Breaches, And Insider Threats. Traditional Security Mechanisms Such As Passwords, Firewalls, VPNs, And Basic Encryption Techniques Are No Longer Sufficient To Address These Evolving Threats. This Paper Proposes A Robust Multi-layered Security Framework Designed To Enhance The Confidentiality, Integrity, And Availability Of Healthcare Data In Cloud Environments. The Framework Integrates Advanced Encryption Standard (AES) For Secure Data Encryption, SHA-256 Hashing For Ensuring Data Integrity, And Fine-grained Access Control Mechanisms To Regulate User Permissions. Additionally, A Zero-Trust Authentication Model Is Incorporated To Continuously Verify Users And Devices, Thereby Minimizing Insider Risks. To Further Strengthen Security, Machine Learning Techniques, Specifically The Isolation Forest Algorithm, Are Employed For Real-time Anomaly Detection And Proactive Threat Identification Within Healthcare Datasets. The Proposed System Also Ensures Compliance With International Data Protection Regulations Such As HIPAA And GDPR, Promoting Secure And Ethical Data Handling Practices. Experimental Analysis Demonstrates That The Framework Significantly Improves Security, Scalability, And Reliability, Making It A Viable Solution For Modern Cloud-based Healthcare Systems.

Published:

10-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

269-275

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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