Virender Dhiman

Phishing attacks remain a persistent threat, exploiting human trust through sophisticated impersonation. In this session, we present a Databricks-based compound agentic system using knowledge graphs, multiple agents, and MLflow to automate triage of reported phishing emails at scale, reducing analyst workload and accelerating threat response. We walk through scalable pipelines built on Spark Structured Streaming and Delta Lake, then describe how our knowledge graph provides long-term memory, storing historical emails to enable reasoning across campaigns. We demonstrate agentic orchestration with LangGraph where specialized agents use tools to analyze headers, sender reputation, body content, URLs, and behaviors before producing triage decisions with reasoning traces. We cover evaluations, observability, including hallucination detection, metrics, and tracking with MLflow. Join us to learn practical patterns that combine multi-agent reasoning with production workflows on Databricks.

Botnet attacks mobilize digital armies of compromised devices that continuously evolve, challenging traditional security frameworks with their high-speed, high-volume nature. In this session, we will reveal our advanced system — developed on the Databricks platform — that leverages cutting-edge AI/ML capabilities to detect and mitigate bot attacks in near-real time. We will dive into the system’s robust architecture, including scalable data ingestion, feature engineering, MLOps strategies & production deployment of the system. We will address the unique challenges of processing bulk HTTP traffic data, time-series anomaly detection and attack signature identification. We will demonstrate key business values through downtime minimization and threat response automation. With sectors like healthcare facing heightened risks, ensuring data integrity and service continuity is vital. Join us to uncover lessons learned while building an enterprise-grade solution that stays ahead of adversaries.