The increasing complexity and frequency of cyber threats necessitate the adoption of advanced machine learning models for threat mitigation. However, the opacity of these models poses challenges in trust, interpretability, and accountability. Explainable AI (XAI) has emerged as a critical solution to enhance transparency in cybersecurity applications. This paper explores the role of XAI in improving trust and interpretability in machine-learned threat detection and mitigation models. By integrating explain ability techniques such as SHAP, LIME, and counterfactual explanations, security analysts can better understand model decisions, identify biases, and ensure compliance with regulatory frameworks. Furthermore, the study evaluates the trade-offs between model performance and explain ability, emphasizing the need for a balanced approach to maintaining accuracy while improving transparency. Case studies demonstrate how XAI techniques have been successfully applied to intrusion detection systems, malware classification, and anomaly detection. The findings highlight that explain ability not only fosters trust among stakeholders but also enhances security decision-making by providing actionable insights. Ultimately, integrating XAI in cybersecurity can bridge the gap between AI-driven automation and human expertise, ensuring robust and accountable threat mitigation strategies.