AI Enabled Predictive Compliance Controls for Healthcare Database Security Governance

Abstract

Artificial Intelligence (AI) technology has changed the way healthcare security is implemented using intelligent prediction of risks, adaptive access control and secure management of Electronic Health Record (EHR). Existing access to control encounter difficulties in identifying new threats, policy enforcement and finding a balance between data protection and efficient processes. In address to solve these issues, the proposed Hybrid Gated Temporal Integer Unit (HGTIU) system can be discussed. Integrated model is the Gated Recurrent Unit (GRU), Temporal Defeasible Logic (TDL) and Mixed-Integer Linear Programming (MILP). Healthcare audit logs and authentications for healthcare are evaluated to determine suspicious activity and any possibility of security breaches. The illustrative prediction accuracy of HGTIU is 93.60% achieved in experimental validation. The permissions, context policies and emergencies are evaluated by TDL, while MILP select the right control measures considering residual risks, costs of intervention and impact on work processes. The research aims to improve healthcare data security, compliance and operational efficiency through intelligent and risk-aware access management.