The increasing frequency of cyber attacks on healthcare organizations, with over 600 incidents reported in 2024, highlights a pressing need for advanced and adaptive security measures. Self- healing networks have emerged as a critical defense mechanism, leveraging closed-loop automation and machine learning for live anomaly detection, automated threat containment, and continuous monitoring. This article examines both vendor-specific and vendor-neutral approaches, including NetOps and AIOps frameworks, which enable organizations to orchestrate selfhealing, interoperability, and proactive management across heterogeneous environments. By integrating open standards and artificial intelligence, these solutions enhance operational resilience, accelerate incident response, and reduce costs. Despite initial implementation complexities, the benefits- including improved security posture, operational efficiency, and long-term cost savingsunderscore the strategic value of adopting self-healing networks and automationdriven frameworks for safeguarding digital infrastructure in healthcare and beyond.
Self-healing networks, Cybersecurity, Closed-loop automation, NetOps, AIOps, Anomaly detection, Machine learning, Automated threat containment, Operational efficiency, Healthcare cybersecurity