Imagine a security camera that can learn and adapt to new threats without needing constant connection to the cloud. That’s the promise of a new AI anomaly detection framework that uses evolving knowledge graphs directly on edge devices. Traditional video anomaly detection (VAD) systems often rely on the cloud to process complex data and update their models. This creates lag and can be problematic in areas with limited connectivity. Researchers have developed a new system called MissionGNN that brings this advanced learning capability to the edge. It uses graph neural networks (GNNs) trained on knowledge graphs (KGs) derived from large language models like GPT-4. Initially, the KG provides a base of knowledge about different anomalies. But the key innovation is the system's ability to continuously adapt the KG directly on the edge device. It does this through a three-phase process of pruning outdated information, altering existing concepts, and creating new nodes representing emerging patterns. This dynamic adaptation is crucial in the real world where security threats constantly change. Instead of requiring cloud intervention for every update, the system learns and evolves in real-time, becoming more accurate and responsive to new types of anomalies. This continuous learning happens without needing a constant connection to the cloud, making it highly efficient for edge computing devices with limited resources. While the system shows great promise, challenges remain. The accuracy of the continuously adapting KG can drift if not carefully monitored, and the interpretation of the evolving knowledge requires sophisticated retrieval methods. However, the potential for real-time, self-learning anomaly detection on the edge is immense. This research could revolutionize security systems, making them more proactive and responsive to the ever-changing landscape of threats in areas like surveillance, evidence analysis, and even violence detection.