Overfitting occurs when a machine learning model, such as those used in cybersecurity for threat detection, learns the training data too well - including its noise and outliers. This excessive tailoring makes the model perform excellently on the training data but poorly on new, unseen data, reducing its real-world effectiveness. In cybersecurity, overfitting can lead to systems that miss novel threats or generate false positives, as the model fails to generalize beyond its specific training examples. Preventing overfitting involves techniques like cross-validation, regularization, and using more diverse datasets to ensure the model remains robust and reliable in detecting new cyber threats.