Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges

Black-box AI models in cybersecurity create a trust problem: While machine learning systems can detect malware with high accuracy, security experts often cannot understand why the system flagged something as dangerous. This lack of transparency is a significant barrier to adopting AI in security-critical environments where decision-making must be justified and auditable.

Explainable AI (XAI) can solve the transparency-performance tradeoff: The paper demonstrates that it's possible to make malware detection systems more interpretable without sacrificing their ability to catch threats effectively, addressing a key concern that transparency might come at the cost of security.

Understanding AI reasoning is essential for effective threat response: When security teams know which specific features or behaviors triggered a malware alert, they can better understand threats, respond more effectively, and improve their overall defense strategies—something that's impossible with unexplainable AI systems.

The field still faces significant open challenges: Despite progress in XAI techniques for malware analysis, the survey identifies that substantial research gaps remain in making these systems consistently interpretable across different types of malware and detection scenarios.

XAI bridges a critical gap in AI adoption for security: By combining strong detection capabilities with explainability, XAI frameworks enable organizations to confidently deploy AI in high-stakes cybersecurity roles where human oversight and understanding are non-negotiable requirements.