COMBINING DEEP LEARNING AND CONVERSATIONAL AI FOR ADVANCED ANOMALY DETECTION IN CYBERSECURITY

Deep learning and conversational artificial intelligence are discussed in this paper, focusing on their possibility of supporting anomaly detection in cyber security systems. While deep learning is effective at processing big data and detecting intricate patterns together with conversational AI, which enables real-time response and active participation - they cover modern threats and challenges reliably. This paper investigates how these technologies enhance the detection of abnormal network traffic, malware, and insider attacks and make fast decisions. The study clarifies that the system is effective, easily scalable, and works well for a wide range of scenarios, some of which are illustrated with such limitations as false positives, data privacy, and model interpretability. Proposed measures can confirm the possibility of developing an effective and adaptive cybersecurity environment.