The growing sophistication of cyberthreats, among others the Distributed Denial of Service attacks, has exposed limitations in traditional rule-based Security Information and Event Management systems. While machine learning-based intrusion detection systems can capture complex network behaviours, their "black-box" nature often limits trust and actionable insight for security operators. This study introduces a novel approach that integrates Explainable Artificial Intelligence-xAI-with the Random Forest classifier to derive human-interpretable rules, thereby enhancing the detection of Distributed Denial of Service (DDoS) attacks. The proposed framework combines traditional static rule formulation with advanced xAI techniques-SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained model. The methodology was validated on two benchmark datasets, CICIDS2017 and WUSTL-IIOT-2021. Extracted rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision, recall, accuracy, balanced accuracy, and Matthews Correlation Coefficient. Experimental results demonstrate that xAI-derived rules consistently outperform traditional static rules. Notably, the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.