Healthcare Cybersecurity: Data Poisoning in the Age of AI
Year: 2024
Type: article
Abstract: This study focuses on the challenges the healthcare sector faces in the wake of increasing digitalization, particularly the growing threat of data poisoning in artificial intelligence systems. Unlike other research, this work delves into the current security protocol weaknesses, highlighting the specific vulnerabilities of healthcare systems and the urgent need for innovative solutions to protect both patients and institutions. Throughout the research, key gaps in security mechanisms are identified and analyzed, showing how these flaws can be exploited by attackers to compromise sensitive information, undermining trust in digital healthcare tools. The methodology combines existing theories with real-world data, allowing for an in-depth and detailed analysis of the risks posed by data poisoning. Advanced cybersecurity strategies are presented, emphasizing the importance of multi-layered detection and mitigation systems designed specifically for the healthcare sector’s needs. Additionally, the broader impact these cybersecurity challenges could have on business processes is explored, revealing how they might slow down the essential digital transformation required to enhance modern healthcare services. This study not only sheds light on security issues in the healthcare sector but also offers practical recommendations to strengthen current defenses. In conclusion, it calls for urgent action to develop new technologies and enforce stricter regulations that safeguard data integrity and ensure a safe and successful digital transition in the face of emerging cybersecurity threats. Received: 9 August 2024 | Revised: 23 September 2024 | Accepted: 8 October 2024 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Edwin Gerardo Acuña Acuña: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition.
Author: Edwin Gerardo Acuña Acuña
Institution: Universidad Latinoamericana de Ciencia y Tecnología
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403498520