Security and Compliance Strategies in Cloud-Based Healthcare Data Solutions
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I2P115Keywords:
Healthcare Cloud Security, Hipaa Compliance, Zero Trust Architecture, Data Governance, Healthcare Interoperability, AI Security, Cloud ComplianceAbstract
Cloud computing has transformed healthcare data management by enabling scalable analytics, interoperability, artificial intelligence (AI)-driven insights, and cost-efficient infrastructure modernization. However, healthcare data is highly sensitive and regulated under frameworks such as HIPAA, HITECH, GDPR, and emerging state-level privacy laws. The migration of electronic health records (EHRs), pharmacy benefit data, claims, imaging, and real-world evidence (RWE) to cloud environments introduces complex security, governance, and compliance challenges. This paper presents a comprehensive security and compliance framework for cloud-based healthcare data solutions. The proposed model integrates zero-trust architecture, encryption lifecycle management, data governance automation, policy-as-code enforcement, and AI-driven anomaly detection. We also introduce a layered reference architecture aligned with healthcare interoperability standards (HL7 FHIR, X12, DICOM) and cloud-native security controls. The paper concludes with implementation strategies, compliance mapping, and future directions in confidential computing and privacy-preserving analytics.
Downloads
References
[1] U.S. Department of Health and Human Services (HHS), “Standards for the Protection of Electronic Protected Health Information (Security Rule),” 45 C.F.R. Part 160 and Subparts A and C ossf Part 164, 2023.
[2] U.S. Department of Health and Human Services (HHS), “HITECH Act Enforcement Interim Final Rule,” 74 Fed. Reg. 56123–56128, 2009.
[3] European Parliament and Council of the European Union, “Regulation (EU) 2016/679 (General Data Protection Regulation),” Official Journal of the European Union, L119, pp. 1–88, Apr. 27, 2016.
[4] European Data Protection Board (EDPB), “Guidelines on Consent under Regulation 2016/679,” 2020.s
[5] HL7 International, “FHIR Release 4,” 2019.
[6] National Electrical Manufacturers Association (NEMA), “Digital Imaging and Communications in Medicine (DICOM) Standard,” 2023.
[7] Accredited Standards Committee X12, “ASC X12 Standards for Electronic Data Interchange,” 2022.
[8] National Institute of Standards and Technology (NIST), “Framework for Improving Critical Infrastructure Cybersecurity,” Version 1.1, 2018.
[9] HITRUST Alliance, “HITRUST Common Security Framework (CSF),” 2023.
[10] ISO/IEC 27001:2022, “Information Security, Cybersecurity and Privacy Protection Information Security Management Systems Requirements,” International Organization for Standardization, 2022.
[11] U.S. Department of Health and Human Services, Office for Civil Rights, “Breach Portal: Notice to the Secretary of HHS Breach of Unsecured Protected Health Information,” 2023.
[12] Verizon, “2023 Data Breach Investigations Report,” 2023.
[13] National Institute of Standards and Technology (NIST), “Security and Privacy Controls for Information Systems and Organizations,” SP 800-53 Rev. 5, 2020.
[14] ENISA, “Threat Landscape for Health Sector,” European Union Agency for Cybersecurity, 2023.
[15] E. R. Longoni, A. Bonezzi, and C. K. Morewedge, “Resistance to medical artificial intelligence,” J. Consumer Res., vol. 46, no. 4, pp. 629–650, 2019.
[16] A. Rajkomar, J. Dean, and I. Kohane, “Machine learning in medicine,” New England Journal of Medicine, vol. 380, no. 14, pp. 1347–1358, 2019.
[17] J. S. Kahn et al., “Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD),” Annals of Internal Medicine, vol. 162, no. 1, pp. 55–63, 2015.
[18] National Institute of Standards and Technology (NIST), “Artificial Intelligence Risk Management Framework (AI RMF 1.0),” 2023.
[19] N. Papernot et al., “Semi-supervised knowledge transfer for deep learning from private training data,” Proc. Int. Conf. Learning Representations (ICLR), 2017.
[20] K. Bonawitz et al., “Practical secure aggregation for federated learning on user-held data,” Proc. ACM CCS, pp. 1175–1191, 2017.
[21] D. Gunning and D. Aha, “DARPA’s Explainable Artificial Intelligence (XAI) Program,” AI Magazine, vol. 40, no. 2, pp. 44–58, 2019.
[22] U.S. Department of Health and Human Services, “HIPAA Security Rule,” 45 C.F.R. §164.312, 2023.
[23] NIST, Artificial Intelligence Risk Management Framework (AI RMF 1.0), 2023.
[24] European Parliament, EU Artificial Intelligence Act, 2024.
[25] U.S. FDA, Good Machine Learning Practice for Medical Device Development, 2021.
[26] WHO, Ethics and Governance of Artificial Intelligence for Health, 2021.
[27] U.S. FDA, Software as a Medical Device (SaMD): Clinical Evaluation Guidance, 2017.
[28] ISO 14971:2019, Medical Devices – Application of Risk Management to Medical Devices.
[29] IEC 62304:2006+A1:2015, Medical Device Software – Software Lifecycle Processes.
