• Provides a comprehensive taxonomy of deep anomaly detection models in AIOps, categorizing them into four methodological groups with special attention to LLM-based techniques.
• Reviews applications across network traffic monitoring, system log analysis, cloud/edge service provisioning, and IoT security, bridging algorithmic research and practical deployment.
• Highlights the limitations of black-box deep learning models in operational settings, emphasizing the need for explainable and robust AD systems.
• Identifies future research directions, including integrating LLMs for multimodal anomaly detection and improving generalization to heterogeneous temporal data.