Data leakage poses a significant threat to modern security systems, leading to unauthorized access and privacy breaches. Deep learning models have shown promise in detecting such anomalies; however, their black-box nature raises concerns regarding trust and interpretability. This paper explores the role of Explainable AI (XAI) in enhancing transparency and trust in deep learning-based data leakage detection. Various explainability techniques, including SHAP, LIME, and Grad-CAM, are integrated into a security framework to provide interpretability while maintaining detection accuracy. The proposed architecture bridges the gap between AI-driven security solutions and human decision-making, enabling security analysts and compliance officers to make informed assessments. Additionally, the study evaluates different XAI approaches based on accuracy, interpretability, and scalability to identify optimal techniques for real-world security applications. The findings highlight the importance of balancing explainability with performance to ensure robust and trustworthy cybersecurity solutions.