Authors : Jaya Chandra Myla

Volume/Issue : Volume 10 - 2025, Issue 3 - March

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Google Scholar : https://tinyurl.com/4rt7hbcz

Scribd : https://tinyurl.com/52pdsmcx

DOI : https://doi.org/10.38124/ijisrt/25mar039

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : The healthcare industry faces critical security and compliance challenges due to stringent regulations and the sensitivity of patient data. The adoption of DevOps in healthcare IT infrastructures has increased the need for robust security measures and automated compliance checks. This research explores the integration of Artificial Intelligence (AI)- driven threat detection and automated compliance verification to enhance security in Healthcare DevOps. AI-powered systems provide real-time monitoring, anomaly detection, and automated responses, reducing security risks and ensuring regulatory compliance. The paper discusses the architecture, methodologies, challenges, and future directions for AI- enhanced security automation in healthcare DevOps environments.

Keywords : Healthcare DevOps, AI-driven Threat Detection, Automated Compliance, Security Automation, Regulatory Compliance.

References :

1. Smith, K., & Jones, P. (2023). AI-Driven Threat Detection in Healthcare IT. IEEE Transactions on Security.
2. Brown, T., & Wilson, L. (2024). Automated Compliance in DevOps Pipelines. Cybersecurity Journal, 29(2).
3. Garcia, R., Martin, A., & Liu, S. (2023). Predictive Analytics for Cybersecurity in Healthcare. Springer.
4. Kim, H., & Lee, J. (2024). Natural Language Processing for Compliance Monitoring. ResearchGate.
5. Williams, B., Taylor, S., & Johnson, M. (2024). Machine Learning for Anomaly Detection in DevOps. Journal of Computer Security, 18(5).