Unlocking AI: Meeting Analysts' Dynamic Needs for Explainability in Cyber Defense

As cyberattacks become more sophisticated and prevalent, analysts face several new challenges, such as a data influx and a high percentage of false alerts. Many AI-driven tools have been utilized in detection systems to help analysts find anomalies. However, the black-box nature of many AI models makes it difficult for analysts to utilize them effectively. This leads to a growing need for Explainable AI (XAI) to make AI models more transparent. However, there is a lack of personalized XAI that would enable analysts to receive explanations tailored to their needs. To address this problem, we present an approach for identifying analyst's need for explainability based on the context information of their current analysis progress.

Chen Zhong is an assistant professor of cybersecurity in the Sykes College of Business at The University of Tampa. Her current research interests include cybersecurity analytics, intelligent systems, and explainable AI. She received her Ph.D. degree in Information Sciences and Technology from Pennsylvania State University. Contact her at czhong@ut.edu.