As encryption becomes ubiquitous across networks and applications, defenders face a growing paradox: while encryption protects user privacy, it also blinds traditional security tools. Intrusion detection systems, data loss prevention tools, and firewalls now struggle to detect threats hiding within encrypted flows like TLS 1.3 or VPN tunnels.

This talk presents a novel AI-driven approach to regain visibility without decrypting traffic. By analyzing encrypted metadata such as packet sizes, flow directionality, timing, and TLS fingerprints, we trained machine learning models to detect threats, including command-and-control channels and data exfiltration attempts, without compromising encryption. Attendees will gain insights into feature engineering, model selection, deployment strategies, and real-world applications of this technique.