Learn how to apply proven DevSecOps security practices to the emerging field of MLSecOps in this 15-minute conference talk. Explore the evolution from DevOps to DevSecOps and understand why traditional security approaches fall short when dealing with AI/ML applications that exhibit dynamic behavior, inherent complexity, and opaque decision-making processes. Discover how ML models' continuous evolution requires adaptive security strategies specifically tailored to AI/ML challenges. Examine MLSecOps methodology that integrates security practices throughout the ML development lifecycle, establishing security as a shared responsibility among ML developers, security practitioners, and operations teams. Gain insights into early identification and mitigation of security risks to develop secure and trustworthy ML models that address the unique vulnerabilities present in machine learning systems.