5 Common Pitfalls in AI Anomaly Detection and How to Avoid Them

Learning from Common Mistakes in Anomaly Detection Systems Building anomaly detection systems looks straightforward in tutorials: load data, train a model, deploy, and watch it catch problems. Reality proves far messier. After reviewing dozens of failed deployments and interviewing teams who struggled with production systems, clear patterns emerge in where implementations go wrong. Understanding these pitfalls before you encounter them can save months of frustration and costly mistakes.