A multi-layered cryptographic trust reinforcement model against AI-driven threat propagation and zero-day cloud vulnerabilities in healthcare data ecosystems

Abstract

The healthcare cloud ecosystem is getting vulnerable to cyberattacks, zero-day attacks and being plagued by AI-driven attacks, hence the need to use operationally feasible, solid, scientific in background security architectures. The authors of this paper introduce a Multi-Layered Cryptographic Trust Reinforcement (MCTR) framework, which unites hierarchical quantum-resistant cryptography, decentralized trust validation through blockchain and AI-based anomaly detector into an adaptive and coordinated security framework. In contrast to the current hybrid healthcare security models, the offered solution provides mathematically controlled trust development, coordinated multi-node zero-day mitigation, and blockchain-controlled forensic transparency. Large-scale simulations with real-world healthcare data show that the threat detection rate is 95-98%, the false positive rate does not surpass 2.5%, blockchain throughput is over 130 transactions per second, and blockchain is over 91% effective against zero-day attacks with a reasonable level of latency. These results prove that MCTR is technically sound, cannot be computationally unfeasible, and practically can be used in real-time healthcare settings.