Anomaly detection is a critical tool for technology leaders aiming to efficiently allocate resources toward genuine threats while minimizing false alarms. Different types of anomalies, such as point, contextual, collective, spatial, time-series, group, and trend anomalies, each require specific detection techniques that range from traditional statistical methods to advanced machine learning and neural network-based approaches. These methods help to identify irregularities across various sectors, including healthcare, cybersecurity, retail, energy, the public sector, finance, and manufacturing, by analyzing data patterns to preemptively address potential issues. As the field evolves, advancements in AI and machine learning, including real-time processing, privacy-preserving methods, and self-supervised learning, are enhancing anomaly detection's ability to provide timely, secure, and accurate insights. These developments allow organizations to anticipate and mitigate problems proactively, integrating anomaly detection more deeply into business processes to maintain operational efficiency and data privacy.