Making AI-Based Detection Transparent and Trustworthy

Explainable AI (XAI) is a core component of T9 Detect, designed to enhance the transparency and reliability of AI-driven malicious traffic detection. Rather than operating as a black-box system, T9 provides interpretable insights into how detection decisions are made.

By exposing key factors that influence each decision, XAI enables analysts to understand why a specific flow or session was classified as malicious, supporting informed validation and investigation.

Key Goals of XAI (for Malicious Network Detection)

• Traffic-Level Transparency
• Revealing which traffic characteristics—such as payload patterns, flow statistics, or session behaviors—most strongly influenced the detection decision.
• Behavior-Oriented Interpretability
• Enabling analysts to interpret model outputs in terms of concrete network behaviors rather than abstract model features.
• Detection Confidence and Validation
• Supporting analyst-driven validation by explaining why a specific flow or session was classified as malicious, including borderline or uncertain cases.
• Operational Accountability
• Providing traceable and reviewable explanations that can be used for incident reporting, rule refinement, and responsible operation of AI-based network defense systems.

Figure Key goals of XAI in malicious network detection: transparency, interpretability, trust, and accountability.