XGBoost is a gradient-boosted decision tree algorithm optimized for structured, tabular data. It builds many small trees in sequence, with each tree correcting errors from the previous ones. The result is often strong accuracy on mixed numeric and categorical features, especially when the data is cleanly labeled and well engineered.
In cyber security, XGBoost is widely used for detection, risk scoring, and classification because it can handle logs, alerts, and other tabular signals efficiently. It also introduces security concerns: poisoned training data, corrupted features, or shifting attacker behavior can degrade predictions, while weak access control around the training pipeline can let an adversary influence model output. Defenders often pair XGBoost with validation, audit logs, and drift monitoring so the model remains reliable as threat patterns change. In safety-critical systems such as healthcare decision support, those controls are essential because the model’s score can shape real-world action even when it is not the final authority.