<p>Anomaly detection in synthetic aperture radar (SAR) images is an important task with numerous applications, including damage assessment, oil spill detection, and land use classification. It is also a crucial pre-task for the detection and forecast of natural catastrophes (NatCat), as faulty data can lead to inaccurate predictions and potentially dangerous situations. Traditional methods for anomaly detection in SAR data often rely on manual inspection, which can be laborious and subjective. An AI-based approach to anomaly detection has the potential to quickly and automatically detect issues in SAR data, improving efficiency and accuracy. One advantage of using AI for anomaly detection is the ability to identify patterns and anomalies that may be difficult for humans to discern, including those that are subtle or not immediately apparent. In addition, an AI-based approach can be more objective and less prone to human bias compared to manual inspection. An AI-based anomaly detection system can also be highly scalable and adaptable, making it a flexible and valuable tool for anomaly detection in SAR images. We propose to present how an AI-based approach can be used to automatically detect different types of anomalies in SAR data to avoid faulty data being used in critical applications.  Overall, an AI-based anomaly detection system has the potential to significantly improve the efficiency and accuracy of anomaly detection in this domain and could have a wide range of applications in other fields also.</p>