XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions

AI's "black box" problem undermines cybersecurity trust: Traditional AI models used to detect cyberattacks often cannot explain their decisions, making it difficult for security professionals to understand why a system flagged a particular threat as dangerous—a critical gap when human judgment and accountability are essential in security operations.

Explainable AI (XAI) enables transparency in threat detection: XAI techniques help visualize and extract information from AI security models with "optimum transparency," allowing developers and security teams to trace the reasoning behind specific threat predictions and build confidence in automated defense systems.

XAI has significant potential for predicting and preventing attacks: The paper identifies XAI as having "immense potential" for cybersecurity applications, suggesting that making AI decision-making processes visible and understandable can improve both the effectiveness and reliability of attack prediction systems.

Implementation challenges require ongoing research: The paper highlights that traditional AI techniques face associated challenges in cybersecurity contexts, indicating that organizations cannot simply deploy standard AI models—they need continued research and development to properly implement XAI solutions that work in real security environments.

Industry and research projects offer practical lessons for responsible deployment: By examining existing XAI implementations across both research projects and industry applications, the paper identifies lessons learned that can guide future development, suggesting that real-world experience is crucial for establishing best practices in responsible AI for cybersecurity.