Improving Patient Data Privacy and Authentication Protocols against AI-Powered Phishing Attacks in Telemedicine

Abstract

Telemedicine’s rapid expansion has improved healthcare accessibility but has also increased cybersecurity risks, particularly AI-powered phishing attacks that exploit authentication vulnerabilities. Patient data breaches are rising due to sophisticated phishing schemes targeting healthcare providers and patients. This study analyzes the impact of AI-driven phishing breaches using data from the HHS Breach Reports, Verizon DBIR, IBM Cost of a Data Breach Report, and PhishTank Open Phishing Dataset. Employing trend analysis, logistic regression, ANOVA, and machine learning classification, the findings reveal a 60% increase in patient record exposure due to AI-powered phishing since 2021, with credential theft contributing most to authentication failures (coefficient = 1.75). The study also finds that blockchain authentication reduces financial losses to $4.5M per breach, significantly lower than the $12M incurred by unprotected organizations. AI-based phishing detection achieves a recall rate of 90.5% but suffers from a 47.6% false-negative rate, indicating the need for refinement. Recommendations include implementing adaptive AI-driven threat detection, behavioral biometrics, blockchain authentication, and stronger regulatory oversight.