Intrusion Detection Systems (IDS) play an important role in protecting information systems by detecting unauthorized access and malicious activities. In response to increasingly complex cyber threats, traditional intrusion detection systems have evolved to include advanced Artificial Intelligence (AI) techniques. However, the opaque structure of many AI models, especially deep learning-based ones, called "black boxes", poses significant challenges in understanding and trusting the decision-making processes of intelligent systems. Explainable Artificial Intelligence (XAI) emerges as a solution to this problem by providing transparency and explainability to complex and opaque models. In this study; XAI is examined in general, XAI and its applications in IDSs are discussed, opportunities, challenges and areas where further research is needed in the field are examined and the integration of XAI into IDS is discussed and how explainable models can increase the efficiency, reliability and accountability of intrusion detection is demonstrated. It is also shown that incorporating XAI into IDS facilitates better decision making and compliance with regulatory standards.