A hybrid approach to data security in ai healthcare systems: a comprehensive review and conceptual framework

Artificial intelligence (AI) integration in healthcare platforms offers significant opportunities for improving patient care while simultaneously introducing critical security vulnerabilities. In 2024, 275 million individuals were affected by healthcare data breaches, underscoring the urgent need for robust protection mechanisms. This paper explores innovative strategies for securing sensitive healthcare data in the Industry 4.0 era, focusing on blockchain, zero-knowledge proofs (ZKP), and honeypots as primary defenses against adversarial attacks on AI/ML models. We introduce an adaptive mathematical model for security scoring incorporating dynamic weights based on real-time threat levels. Python-based simulations validate the proposed hybrid framework, demonstrating up to 15% improvement in security scores under attack conditions and reaching 90% security scores against sustained attacks compared to traditional approaches. An extensive literature review synthesizes recent (2020–2025) research on AI-driven cybersecurity in healthcare. Our findings underscore the need for multidimensional security frameworks combining blockchain for data integrity, ZKP for privacy preservation, and AI-enhanced honeypots for threat detection. Concrete recommendations are provided for the healthcare community regarding the adoption of advanced security measures aligned with Industry 4.0 standards.