Artificial Intelligence Voice Cloning in Healthcare Cybersecurity: Challenges, Opportunities, and Protective Strategies
Abstract
AI voice cloning has been one of the most transformative technologies in the health sector, improving patient-provider communication and enhancing operations in telehealth and remote monitoring. However, it comes with its own unique cybersecurity risks, including identity theft and unauthorized access. This paper focuses on the risks of cybersecurity threats in healthcare with relation to the use of AI voice cloning and assesses the efficiencies of current protective measures before proposing additional recommendations for enhanced security for data in the health sector. Based on mixed-method research, the study includes a systematic literature review and interviews with HCI and IT professionals, cybersecurity experts, and AI technology developers. It also accents current threats as well as possible solutions to the emerging vulnerabilities. The study shows that even though the use of AI for voice cloning has massive benefits, it creates significant security issues. These are: the use of voice phasing, unauthorized operations, and falsification of patient identity. The study calls for integrated security measures to fashion out secure AI systems, embrace enhanced modes of authentication, and conduct AI system check-ups frequently to avoid compromised voice cloning technology in healthcare.
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Alabdan, R. (2020). Phishing attacks survey: Types, vectors, and technical approaches. Future internet, 12(10), 168.
Genelza, G. G. (2024). A systematic literature review on AI voice cloning generator: A game-changer or a threat? Journal of Emerging Technologies, 4(2), 54-61.
Jimmy, F. N. U. (2024). Cyber security Vulnerabilities and Remediation Through Cloud Security Tools. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2(1), 129-171.
Koffi, E. (2023). VOICE BIOMETRICS FUSION FOR ENHANCED SECURITY AND SPEAKER RECOGNITION: A COMPREHENSIVE REVIEW. Linguistic Portfolios, 12(1), 6.
Lewandowski, R., Goncharuk, A. G., & Cirella, G. T. (2021). Restoring patient trust in healthcare: medical information impact case study in Poland. BMC Health Services Research, 21(1), 865.
O’Kane, P., Smith, A., & Lerman, M. P. (2021). Building transparency and trustworthiness in inductive research through computer-aided qualitative data analysis software. Organizational Research Methods, 24(1), 104-139.
DOI: https://doi.org/10.53889/aihitj.v1i1.536
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