The Mega Healthcare Data Breaches in the United States (2009 – 2023): A Comparative Document Analysis

This paper presents a comprehensive analysis of the predominant healthcare data breaches in the United States from October 2009 to September 2023, utilizing a mixed-methods approach centered on seven publicly available breach reports. It aims to identify patterns, common factors, and measures to enhance cybersecurity within the sector. Through comparative document analysis, the study examines the nature, causes, and repercussions of these breaches, recognizing external attacks, internal errors, and software vulnerabilities as critical weaknesses. The consequences range from financial and reputational damage to erosion of patient trust. The findings stress the necessity for improved preventive strategies, bolstering of security practices, employee training, vendor oversight, and effective incident response mechanisms. The paper also offers insights into the legal and ethical implications of breaches. It suggests robust cybersecurity measures, including the adoption of emerging technologies like blockchain and AI/ML to deter threats. The recommendations guide healthcare organizations toward establishing robust protections for sensitive health data, ensuring regulatory compliance, and facilitating continuity of trust and care. The paper serves as a call to action for ongoing study into the multidimensional impact of data compromises in healthcare.

Data Loss Prevention Management and Control: Inside Activity Incident Monitoring, Identification, and Tracking in Healthcare Enterprise Environments

As healthcare data are pushed online, consumers have raised big concerns on the breach of their personal information. Law and regulations have placed businesses and public organizations under obligations to take actions to prevent data breach. Among various threats, insider threats have been identified to be a major threat on data loss. Thus, effective mechanisms to control insider threats on data loss are urgently needed. The objective of this research is to address data loss prevention challenges in healthcare enterprise environment. First, a novel approach is provided to model internal threat, specifically inside activities. With inside activities modeling, data loss paths and threat vectors are formally described and identified. Then, threat vectors and potential data loss paths have been investigated in a healthcare enterprise environment. Threat vectors have been enumerated and data loss statistics data for some threat vectors have been collected. After that, issues on data loss prevention and inside activity incident identification, tracking, and reconstruction are discussed. Finally, evidences of inside activities are modeled as evidence trees to provide guidance for inside activity identification and reconstruction