Enhancing Intrusion Detection Systems (IDSs) using transparent and explainable artificial intelligence (XAI) solutions entails making AI-based intrusion detection models more reliable, effective, and interpretable. Conventional intrusion detection systems frequently depend on “black boxes,” such as rule-based systems or machine learning models, which make it challenging for security analysts to comprehend the reasoning behind particular choices. We may increase the dependability, comprehension, and actionability of these systems by implementing explainability and transparency. A range of machine learning and deep learning models are employed in intrusion detection systems (IDS) to identify hostile activity. The particular needs of the system, including accuracy, interpretability, scalability, and the capacity to manage various data types (e.g., network traffic, system logs), determine which model is best.