AI-augmented workflow resilience framework for cybersecurity risk mitigation in hospital AI systems

GUMMADI, HARI SURESH BABU (2025) AI-augmented workflow resilience framework for cybersecurity risk mitigation in hospital AI systems. World Journal of Advanced Research and Reviews, 26 (2). pp. 1175-1182. ISSN 2581-9615

The AI-Augmented Workflow Resilience Framework represents a transformative approach to cybersecurity in healthcare environments utilizing artificial intelligence systems. It examines how the integration of AI into hospital settings creates unique security vulnerabilities that traditional cybersecurity methods fail to adequately address. The proposed framework embeds security mechanisms directly into clinical and administrative workflows through five interconnected layers: Continuous Workflow Monitoring, AI-Specific Threat Detection, Healthcare Context Interpretation, Adaptive Response Orchestration, and Continuous Learning and Improvement. Implementation across diverse healthcare facilities—including community hospitals, regional medical centers, and academic medical centers—demonstrated the framework's effectiveness in enhancing security while preserving operational efficiency. Evaluation results reveal substantial improvements in threat detection capabilities, particularly for AI-specific vulnerabilities such as adversarial attacks and model manipulation. The context-aware approach significantly reduced false positives and workflow disruptions while maintaining essential clinical functions during security incidents. Technical performance analysis confirmed reasonable resource requirements with favorable scalability characteristics. It addresses a critical gap in healthcare cybersecurity by creating an integrated approach that protects advanced AI systems while supporting rather than impeding the delivery of patient care.