Anomaly-detection systems based on artificial intelligence are increasingly used in cybersecurity and industrial environments, but their outputs are often difficult for human analysts to interpret. This paper explores human-centered explainability approaches for anomaly detection through the generation of natural-language explanations that help analysts understand model predictions and anomalous behaviors.
The work investigates methodologies for translating complex machine-learning outputs into interpretable and context-aware explanations that can support decision-making processes in cybersecurity operations. By combining anomaly-detection techniques with natural-language explanation mechanisms, the proposed approach improves transparency, usability, and analyst trust in AI-assisted systems.
The results demonstrate the potential of natural-language explainability frameworks to bridge the gap between advanced anomaly-detection models and human-centered cybersecurity analysis.