AI-Powered Cybersecurity for Safeguarding Electronic Health Records from Deepfake Biometric Attacks
Keywords:
Electronic Health Records (EHRs), Deepfake Biometric Attacks, AI-Powered Cybersecurity, Biometric Authentication, Generative Adversarial Networks (GANs), Liveness Detection, Behavioural Authentication, Zero Trust, Blockchain Identity Management, Deepfake Detection Models, Synthetic Fingerprint Spoofing, Voice Authentication Attack, Healthcare Cybersecurity, Machine Learning Security, Regulatory Compliance (HIPAA, GDPR, NIST 800-63B)Abstract
The adoption of biometric authentication in Electronic Health Records (EHR) systems enhances security but also introduces new vulnerabilities, particularly from deepfake biometric attacks. This paper introduces an AI-driven cybersecurity framework integrating deepfake detection models, liveness verification, behavioral authentication, Zero Trust security, and blockchain identity management to mitigate these risks. Unlike traditional authentication methods, the proposed framework ensures real-time biometric verification, adaptive risk-based access control, and decentralized identity validation to prevent unauthorized access and identity fraud. By leveraging machine learning algorithms, generative adversarial networks (GANs), and AI-powered anomaly detection, this study demonstrates an improved authentication success rate and a 45% reduction in unauthorized access attempts, ensuring regulatory compliance with HIPAA, GDPR, and NIST 800-63B standards
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