AI- Driven Risk Assessment for Enhancing Third Party Vendor Security in Healthcare Systems
Valerie Ojinika Ejiofor
*
University of Tampa, 401 W Kennedy Blvd, Tampa, FL 33606, United States of America.
Akinde Michael Ogunmolu
Failure Modeling & Simulation, Cyber-Physical Systems, Energy Security Researcher, Texas A&M University, 700 University Blvd, Kingsville, TX 78363, United States of America.
Michael Olayinka Gbadebo
Cavendish University Zambia, Corner of and Elizabeth, Great N Rd, Lusaka, Zambia.
Sunday Abayomi Joseph
Data Privacy, Blockchain Strategy & Management, Ashland University, 401 College Avenue, Ashland, OH 44805, United States of America.
Temilade Oluwatoyin Adesokan-Imran
University of Ibadan, Oduduwa Road, 200132, Ibadan, Oyo, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This study investigates the application of artificial intelligence (AI) in managing cybersecurity risks associated with third-party vendors in healthcare systems. With third-party breaches accounting for a significant proportion of healthcare data compromises, this research seeks to answer a central question: To what extent does AI reduce the frequency, cost, and impact of vendor-related data breaches in healthcare institutions? Specifically, it evaluates whether AI adoption improves breach detection speed, reduces containment time, and lowers breach-related financial losses. To address these objectives, the study analyzes three reputable datasets: the U.S. Department of Health and Human Services (HHS) OCR Breach Portal (2018–2024), the HIMSS Cybersecurity Survey, and the IBM Cost of a Data Breach Report. Descriptive statistics were used to assess breach frequency and vendor involvement. A chi-square test evaluated the statistical association between AI adoption and breach incidence, while multiple linear regression measured AI’s impact on breach cost, time to detect, and time to contain. The results reveal that organizations using AI reported a significantly lower breach incidence (22.5%) compared to non-adopters (60%). Regression analysis further shows that AI adoption reduces breach costs by $2.84 million, shortens detection time by 24.47 days, and containment time by 20.62 days. These findings support the integration of AI as a strategic tool for real-time risk mitigation and operational resilience. The study recommends regulatory enforcement of AI adoption in third-party risk governance and the inclusion of AI clauses in vendor contracts to strengthen data protection in healthcare.
Keywords: Artificial Intelligence, healthcare cybersecurity, third-party vendors, risk assessment, data breaches