AI-Driven HIPAA Compliance Enforcement with Terraform and Azure Policy for Continuous Regulatory Monitoring

Abstract

Healthcare cloud compliance is difficult to maintain when Terraform deployments, Azure resource policies, and runtime monitoring operate as separate control layers. This article proposes an AI-driven HIPAA compliance enforcement framework that connects Terraform-based infrastructure validation, Azure Policy enforcement, and machine learning-based regulatory drift monitoring into one continuous governance workflow. The framework maps HIPAA-relevant controls to cloud configuration signals, including RBAC assignments, managed identities, diagnostic logging, encryption settings, private endpoints, backup policies, and telemetry changes. Results show that compliance detection accuracy, policy enforcement coverage, and regulatory drift prediction improve across monitoring cycles, while resource-group analysis identifies variation in access-control risk, encryption compliance, and audit readiness across Azure healthcare environments. The study demonstrates that HIPAA compliance can move from periodic manual review toward continuous, evidence-driven regulatory monitoring when infrastructure code, cloud policy, and AI-based drift prediction are integrated.