A Survey on Explainable Artificial Intelligence for Cybersecurity

Black-box AI poses real risks in cybersecurity: The paper identifies the "black-box nature" of AI models as a significant concern for critical security applications, meaning security teams often cannot understand why an AI system flagged a threat or made a security decision—a serious problem when lives and infrastructure may be at stake.

Explainable AI can improve threat understanding: XAI methods enable security professionals to better understand how cyber threats behave and why they're dangerous, moving beyond simply knowing "the AI detected something" to actually comprehending the threat itself.

XAI enables better defense design: By making AI decisions transparent and interpretable, organizations can design more effective cybersecurity defenses rather than blindly trusting algorithmic recommendations, allowing human experts to validate and improve security strategies.

Current XAI methods have limitations in cybersecurity: The survey acknowledges that existing explainability approaches face specific challenges when applied to network security problems, suggesting that generic XAI solutions may not fully address cybersecurity's unique demands.

This is an emerging field requiring further development: The paper describes XAI for cybersecurity as "rapidly growing" with "promising directions for future research," indicating that responsible, explainable AI security systems are still being developed and improved rather than fully mature solutions.