Abstract
Data breaches in healthcare are increasingly caused by insider threats, yet traditional logging systems remain vulnerable to tampering. This paper proposes a hybrid security framework for medical data in Kazakhstan, combining blockchain for immutable log storage with machine learning for automated anomaly detection. We introduce a dual-backend architecture where a private Ethereum network secures access logs, while a separate analysis module utilizes supervised learning algorithms to identify suspicious behaviour patterns in real-time. To validate the system, we generated a region-specific synthetic dataset compliant with Kazakhstani national identification standards (IIN). Experimental results demonstrate that the system effectively secures audit trails against modification and detects anomalous access patterns with high accuracy. The proposed solution addresses the critical gap between data integrity and proactive threat detection, offering a scalable architecture compliant with data privacy regulations.