Foundation Models for Anomaly Detection: Vision and Challenges

Abstract As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular patterns that may signal critical issues. Recently, foundation models (FMs) have emerged as a powerful tool for advancing anomaly detection. They have demonstrated unprecedented capabilities in enhancing anomaly identification, generating detailed data descriptions, and providing visual explanations. This survey presents the first comprehensive review of recent advancements in FM‐based anomaly detection. We propose a novel taxonomy for classifying FMs in anomaly detection tasks, based on the roles they play within the detection pipeline. Specifically, we categorize FMs as encoders, detectors, or interpreters, reflecting whether they are used for feature extraction, anomaly detection, or result interpretation, respectively. We provide a systematic analysis of state‐of‐the‐art methods and discuss key challenges in leveraging FMs for improved anomaly detection. We also outline future research directions in this rapidly evolving field.