As cyber threats grow more evasive, traditional detection methods often fall short. This session explores how to leverage Databricks as a Security Lakehouse to build ML-driven defenses that adapt to adversary tactics. We’ll dive into creating adversary-aware models that detect anomalies, recognize lateral movement, and retrain continuously as attackers evolve. Using Databricks capabilities of Delta Lake, LakeFlow pipelines, and MLlib, the security teams can operationalize scalable detection without drowning in noise

Attendees will learn practical steps to develop resilient ML workflows, mitigate adversarial risks, and empower SOCs with data-driven insights that anticipate and counter attacker innovations in real time