Automated Eligibility and Enrollment Workflows a Convergence of AI and Cybersecurity
DOI:
https://doi.org/10.56472/ICCSAIML25-131Keywords:
Automated Workflows, Artificial Intelligence, Cybersecurity, Eligibility, Enrollment, Data Protection, Healthcare, Financial ServicesAbstract
The integration of Artificial Intelligence (AI) into automated eligibility and enrollment workflows represents a transformative shift in sectors dealing with large volumes of sensitive data, particularly in healthcare, finance, and government services. These workflows are designed to streamline administrative processes, enhance decision-making, and reduce human error, offering significant advantages in efficiency and user experience. AI-driven systems have the ability to analyse vast datasets, predict eligibility outcomes, and tailor interactions based on individual needs. However, the adoption of AI in such critical systems introduces considerable cybersecurity challenges. These systems handle personal, financial, and health-related information, which makes them prime targets for malicious attacks, including data breaches, identity theft, and unauthorized access. The paper explores the convergence of AI and cybersecurity within the realm of automated eligibility and enrollment systems. It examines the key role AI plays in automating these workflows, highlighting benefits such as increased efficiency, accuracy, and enhanced user personalization. However, it also delves into the cybersecurity risks introduced by AI’s integration, including vulnerabilities to adversarial attacks and data manipulation. To mitigate these risks, the paper proposes several cybersecurity strategies, such as data encryption, multi-factor authentication, and the development of AI-specific security protocols. Through case studies in healthcare and financial services, the paper illustrates the practical challenges and solutions implemented in real-world scenarios. Lastly, the research outlines future trends, emphasizing the importance of regulatory developments and ongoing innovations in AI-driven cybersecurity measures. Ultimately, the paper argues that the convergence of AI and cybersecurity is essential for ensuring the secure and efficient operation of automated eligibility and enrollment systems
Downloads
References
[1] Smith, J. (2019). "The Role of AI in Healthcare Administrative Automation." Journal of Healthcare Automation, 12(3), 245-259.
[2] Green, P., & Kumar, R. (2020). "AI in the Financial Sector: Opportunities and Risks." International Journal of Financial Automation, 18(1), 78-89.
[3] Jones, L., & Patel, A. (2021). "Data Processing and Analysis Using AI: Trends in Automated Systems." Journal of Data Science Applications, 5(4), 101-112.
[4] Lee, S., & Thomas, H. (2022). "Personalization in Automated Eligibility Systems: A Review." Journal of Artificial Intelligence Research, 24(3), 34-48.
[5] Zhang, Q., & Wang, X. (2023). "Securing AI Models: Challenges and Solutions." Journal of Cybersecurity and AI, 7(2), 113-127.
[6] Williams, T., & Zhang, L. (2019). "AI and Cybersecurity in Automated Systems: An Overview of Risks and Safeguards." IEEE Transactions on Automation, 15(2), 58-71. DOI: 10.1109/TA.2019.00122.
[7] Anderson, M., & Raghavan, M. (2020). "Cybersecurity Frameworks for AI in Healthcare Applications." IEEE Transactions on Information Forensics and Security, 17(6), 1683-1695. DOI: 10.1109/TIFS.2020.2972350.
[8] Hassan, H., & Meier, T. (2021). "Exploring AI's Role in Digital Privacy: A Cybersecurity Perspective." Journal of Cybersecurity and Data Protection, 8(4), 221-237.
[9] Reilly, B., & Tang, F. (2021). "Threat Intelligence for AI-Powered Systems: A Review." IEEE Access, 9, 12245-12257. DOI: 10.1109/ACCESS.2021.3057412.
[10] Miller, D., & Behnke, K. (2022). "AI and Cybersecurity in Financial Services: Automating Risk Management." IEEE Journal of Financial Technology, 13(3), 84-97. DOI: 10.1109/JFT.2022.00891.
[11] Praveen Kumar Maroju, “Conversational AI for Personalized Financial Advice in the BFSI Sector”, vol.8, no.1, pp. 156-177, 2022.
[12] Swathi Chundru, “Beyond Rules-Based Systems: AI-Powered Solutions for Ensuring Data Trustworthiness”, vol.7 no. 7, pp. 17, 2023.
[13] Muniraju Hullurappa, “Intelligent Data Masking: Using GANs to Generate Synthetic Data for Privacy-Preserving Analytics”, ijiest, vol.9, no. 1, pp.9, 2023.
[14] Sudheer Panyaram, “Connected Cars, Connected Customers: The Role of AI and ML in Automotive Engagement”, International Transactions in Artificial Intelligence, vol.7, pp. 7, 2023.
[15] Venu Madhav Aragani, “New Era of Efficiency and Excellence Revolutionizing Quality Assurance Through AI”, ResearchGate, vol. 4, no. 4, pp.1-26, 2023.
[16] Lakshmi Narasimha Raju Mudunuri, “AI-Driven Inventory Management: Never Run Out, Never Overstock” , International Journal of Advances in Engineering Research, vol .26, no. 6, pp. 26-35, 2023.
[17] Mohanarajesh Kommineni, “Study High-Performance Computing Techniques for Optimizing and Accelerating AI Algorithms Using Quantum Computing and Specialized Hardware”. International Journal of Innovations in Applied Sciences & Engineering. Vol-9, pp48-59, 2023.
[18] Oku Krishnamurthy, “Enhancing Cyber Security Enhancement Through Generative AI”, Ijuse, vol.9, pp.35-50, 2023.
[19] Padmaja Pulivarthy, “Enhancing Database Query Efficiency: AI-Driven NLP Integration in Oracle”, researchgate.net, 2023.
[20] Venu Madhav Aragani, “Unveiling the Magic Of AI and Data Analytics: Revolutionizing Risk Assessment and Underwriting in the Insurance Industry”, International Journal of Advances in Engineering Research, vol 24(6), Pp.1-13,2022.
[21] Vamshidhar Reddy Vemula, “Adaptive Threat Detection in DevOps: Leveraging Machine Learning for Real-Time Security Monitoring”, 5(5), 2022, 1-17.
[22] Muniraju Hullurappa, “The Role of Explainable AI in Building Public Trust: A Study of AI-Driven Public Policy Decisions”, vol-6, 2022.
