Automated Eligibility and Enrollment Workflows a Convergence of AI and Cybersecurity
Year: 2025
Type: conference-paper
Abstract: 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
Author: Gopi Chand Vegineni
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411212678