The chapter investigates the amalgamation of defined security approaches and modern machine-learning techniques inside software development. It aligns with the book’s core theme of utilizing machine learning to transform the development process. The chapter begins by outlining fundamental security concepts and the significance of identifying anomalies in software systems, setting the stage for a proactive approach to threat detection. It delves into various machine learning techniques, such as neural networks and unsupervised learning, that drive innovation in anomaly detection. A detailed analysis highlights the strengths and challenges of each method. The chapter addresses the challenges of integrating these methods into current systems while offering valuable insights into applying machine learning in security protocols by using cases from the real world. It also looks at ethical issues, like algorithmic bias, and how they affect the fairness and reliability of security measures. This chapter concludes by providing comprehensive guidance for project managers and software developers on successfully incorporating machine learning into security plans and guaranteeing safer and more effective software solutions.