Adopting HITRUST and AI for Securing Healthcare Data: A Blueprint for U.S. Medical Facilities
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P110Keywords:
HITRUST CSF (Common Security Framework), AI in Healthcare, Healthcare Data Security, HIPAA Compliance, AI-Powered Risk Models, Machine Learning for Threat Detection, Natural Language Processing (NLP) in Healthcare, Cybersecurity Risk Management, Ethical AI Implementation, AI Assurance Programs, Interoperability in Health IT Systems, Digital Health Transformation, AI-Driven Healthcare AutomationAbstract
Healthcare data security is a critical challenge in the U.S., with increasing threats and stringent compliance requirements. The Health Information Trust Alliance (HITRUST) framework provides a comprehensive set of guidelines for healthcare organizations to safeguard sensitive patient information while adhering to regulatory mandates. However, achieving and maintaining HITRUST compliance is resource-intensive and complex. Artificial Intelligence (AI) offers transformative potential in this domain, enabling enhanced data protection, real-time threat detection, and streamlined compliance processes. This paper explores the integration of AI-driven solutions into HITRUST compliance efforts, presenting a blueprint for U.S. medical facilities to adopt AI technologies to secure patient data effectively. Through encryption, anomaly detection, automated risk assessments, and compliance monitoring, AI can significantly enhance the security posture of healthcare institutions. This work provides actionable insights into the implementation of AI for HITRUST compliance, addressing challenges, limitations, and future trends in securing healthcare data
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[1] A. Alhadidi, N. B. Anuar, S. Razak, and M. A. Almomani, “Securing electronic health records in the cloud: A review of current solutions and open issues,” Journal of Network and Computer Applications, vol. 135, pp. 102–116, 2019.
[2] J. Yoo, A. Kim, and S. W. Kim, “Artificial intelligence-based anomaly detection in medical records using hybrid models,” IEEE Access, vol. 7, pp. 119622–119632, 2019.
[3] R. Anderson and T. Moore, “The economics of information security,” Science, vol. 314, no. 5799, pp. 610–613, 2006.
[4] B. Schneier, Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. New York: W. W. Norton & Company, 2015.
[5] M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science, vol. 349, no. 6245, pp. 255–260, 2015.
[6] HITRUST, “Understanding the HITRUST CSF,” [Online]. Available: https://hitrustalliance.net/csf/.
[7] H. R. Lakkaraju, J. Kleinberg, and J. Leskovec, “A machine learning framework for algorithmic fairness in healthcare,” Proceedings of the 26th International Conference on World Wide Web, 2017.
[8] C. Dwork and A. Roth, “The algorithmic foundations of differential privacy,” Foundations and Trends in Theoretical Computer Science, vol. 9, no. 3–4, pp. 211–407, 2014.
[9] M. E. Johnson, S. B. Goetz, and J. M. Gross, “Security compliance in the healthcare sector: The role of security policies and procedures,” Information Systems Research, vol. 24, no. 2, pp. 419–441, 2013.
[10] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice Hall, 2010.
[11] K. Chen and Y. Wang, “Secure data sharing and access control in cloud-assisted healthcare systems,” IEEE Transactions on Cloud Computing, vol. 7, no. 2, pp. 432–445, 2018.
[12] T. Ristenpart, H. Shacham, and B. Y. Zhao, “Healthcare data security in the era of artificial intelligence,” IEEE Security & Privacy, vol. 16, no. 4, pp. 44–49, 2018.
[13] P. Kaur, D. Kumar, and S. Kumar, “A systematic review of blockchain technology: Applications, security challenges, and future research directions,” IEEE Access, vol. 8, pp. 62474–62488, 2020.
[14] D. D. Clark and D. R. Wilson, “A comparison of commercial and open-source intrusion detection systems,” Computers & Security, vol. 28, no. 8, pp. 1001–1013, 2009.
[15] J. M. Underwood and K. R. Olshansky, “Securing healthcare systems against emerging threats,” Healthcare Management Review, vol. 43, no. 2, pp. 99–109, 2018.
[16] G. A. Kumar and S. Sundaram, “Enhancing healthcare data security through a compliance-driven approach,” Journal of Medical Internet Research, vol. 21, no. 7, pp. 234–245, 2019.
[17] L. Sweeney, “k-Anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 557–570, 2002.
[18] T. Chakraborty and K. J. Lee, “AI-enabled proactive healthcare: Trends and challenges,” Health Informatics Journal, vol. 25, no. 4, pp. 1472–1488, 2019.
[19] N. A. Gagneja and J. P. Singh, “AI-powered threat intelligence systems: A new era in cybersecurity,” Cybersecurity Journal, vol. 17, no. 3, pp. 341–352, 2018.
[20] A. M. Zhang and B. E. Price, “Natural language processing for regulatory compliance in healthcare,” International Journal of Medical Informatics, vol. 132, pp. 103–112, 2019.
[21] J. Lu and L. Sun, “Quantum-safe cryptography for healthcare data protection,” Cryptography Journal, vol. 12, no. 4, pp. 301–318, 2019.
[22] Y. Xu, Z. Yuan, and J. Yang, “Generative adversarial networks for synthetic healthcare data generation,” Journal of Machine Learning Research, vol. 21, no. 89, pp. 1–24, 2020.
[23] B. McMahan and D. Ramage, “Federated learning: Collaborative machine learning without centralized data,” Google AI Blog, [Online]. Available: https://ai.googleblog.com.
[24] M. E. Saleh and A. O. Davis, “Anomaly detection using AI in large healthcare networks,” Health Informatics Research, vol. 27, no. 2, pp. 97–110, 2020.
[25] K. Lee, H. A. Park, and J. Choi, “Automated incident response frameworks in healthcare cybersecurity: An AI perspective,” Cybersecurity and Privacy Review, vol. 18, no. 1, pp. 44–55, 2021.
[26] L. B. Zhao, Y. R. Chang, and F. Wu, “AI-enabled compliance management in healthcare,” Health Data Analytics Journal, vol. 15, no. 2, pp. 88–98, 2021.
[27] R. V. Shapiro and M. N. Taylor, “Risk-based compliance auditing in the healthcare sector,” Healthcare Compliance Review, vol. 19, no. 3, pp. 213–221, 2021.
[28] P. T. Nguyen, M. E. Clark, and J. S. Miller, “Adopting AI for HITRUST compliance: A practical roadmap,” Health IT Review, vol. 9, no. 4, pp. 155–168, 2021.
[29] H. S. Kim and A. J. Smith, “Balancing AI automation and human oversight in healthcare security,” Healthcare Cybersecurity Journal, vol. 10, no. 3, pp. 145–157, 2020.
[30] D. J. Thompson, G. White, and C. Clarke, “Economic challenges in adopting AI for healthcare compliance,” Health Economics Review, vol. 12, no. 1, pp. 55–68, 2020.
[31] A. Mitchell, J. R. Hart, and D. Morris, “Blueprint for integrating AI into HITRUST compliance: Strategies and outcomes,” Health IT Journal, vol. 11, no. 2, pp. 234–250, 2020.
[32] J. Green and T. Kumar, “Reinforcement learning in healthcare data security,” IEEE Transactions on Healthcare Informatics, vol. 14, no. 3, pp. 299–311, 2020.
[33] K. V. Tan and P. Nguyen, “Blockchain and AI in compliance auditing for healthcare,” Journal of Digital Health Security, vol. 8, no. 4, pp. 220–237, 2021.
[34] S. Park and H. Kim, “Federated learning for privacy-preserving compliance monitoring,” Journal of AI Research in Healthcare, vol. 19, no. 1, pp. 45–63, 2021.
[35] M. Evans and G. Wright, “AI-driven regulatory intelligence for dynamic compliance,” RegTech Review, vol. 6, no. 2, pp. 77–89, 2020.
[36] P. Kumar and J. Roberts, “Autonomous AI in incident response: Trends and applications,” Cyber Defense Journal, vol. 12, no. 1, pp. 101–117, 2020.
[37] L. Zhang, W. Chen, and Y. Wu, “Quantum computing and AI in healthcare data security,” Journal of Emerging Technologies in Computing Systems, vol. 18, no. 2, pp. 97–115, 2020.
[38] A. Patel and R. Smith, “Global AI standards for healthcare compliance frameworks,” Global Health IT Standards Review, vol. 7, no. 3, pp. 67–78, 2021.
[39] J. Padhye, V. Firoiu, and D. Towsley, “A stochastic model of TCP Reno congestion avoidance and control,” Univ. of Massachusetts, Amherst, MA, CMPSCI Tech. Rep. 99-02, 1999.
[40] Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, IEEE Std. 802.11, 1997.
[41] Pulivarthy, P. (2022). Performance tuning: AI analyse historical performance data, identify patterns, and predict future resource needs. International Journal of Innovations in Applied Sciences and Engineering, 8(1), 139–155.
[42] P. K. Maroju, "AI-Powered DMAT Account Management: Streamlining Equity Investments and Mutual Fund Transactions," International Journal of Advances in Engineering Research, vol. 25, no. 1, pp. 7–18, Dec. 2022.
