Revolutionizing healthcare cybersecurity a generative AI-Driven MLOps framework for proactive threat detection and mitigation

The exponential growth of digitalization in healthcare has led to unprecedented challenges in securing sensitive data, safeguarding patient privacy, and ensuring system integrity against evolving cyber threats. Traditional cybersecurity measures often struggle to cope with the dynamic nature of modern attacks, particularly in environments where real-time decision-making and adaptive responses are critical. This paper introduces a novel Generative AI-driven MLOps framework designed to address these challenges by combining the power of Generative Adversarial Networks (GANs) with the operational efficiency of Machine Learning Operations (MLOps).The proposed framework leverages generative AI models to simulate diverse and sophisticated cyber-attack scenarios, enabling the training of robust threat detection mechanisms. By integrating MLOps pipelines, the framework ensures seamless deployment, real-time monitoring, and continuous learning to adapt to emerging threats. This approach not only enhances anomaly detection but also automates threat mitigation, significantly reducing response times and minimizing the impact of cyber incidents.The framework was validated using both synthetic and real-world healthcare datasets, demonstrating superior performance in terms of detection accuracy (98%), reduced false positive rates (2%), and faster response times (35% improvement over baseline models). A case study simulating a ransomware attack in a hospital setting revealed the system's ability to neutralize threats with 92% success within seconds of detection. These findings highlight the transformative potential of integrating generative AI with MLOps in healthcare cybersecurity, paving the way for more resilient, adaptive, and scalable security solutions. This research contributes to advancing the state-of-the-art in healthcare cybersecurity while addressing critical gaps in threat detection and mitigation strategies.