Enhancing Transparency and Trust in Cybersecurity: Developing Explainable AI Models for Threat Detection

The increasing reliance on artificial intelligence (AI) in cybersecurity has significantly improved threat detection and response. However, many AI-driven Defense mechanisms function as “black boxes,” making it difficult for security professionals to interpret their decisions. This lack of transparency reduces trust in AI systems and limits their adoption in critical security operations. Despite advancements in explainable AI (XAI), there is a significant research gap in applying XAI techniques specifically to cybersecurity. This study aims to bridge this gap by developing and evaluating explainable AI models for cybersecurity applications. The research employs a combination of interpretable machine learning algorithms, feature attribution methods, and human-in-the-loop approaches to enhance model transparency. Various cybersecurity datasets, including network intrusion detection and malware classification data, are used to assess the effectiveness of these models. Key findings indicate that incorporating explainability techniques improves user trust and facilitates better decision-making without compromising model performance. Additionally, the study highlights the trade-offs between explainability and predictive accuracy, offering insights into optimizing AI models for real-world cybersecurity applications. In conclusion, this research demonstrates that integrating explainable AI into cybersecurity frameworks enhances transparency and user confidence, leading to more effective threat mitigation. Future work will focus on refining these models and developing standardized evaluation metrics for explainability in AI-driven security systems.