Modern data platforms generate enormous volumes of audit records, access logs, and security alerts. However, traditional rule-based monitoring often produces too much noise, while subtle threats such as compromised credentials, privileged-user misuse, abnormal service-account activity, and unauthorized data extraction can remain difficult to identify.

This practical, vendor-neutral session explores how behavioral analytics can help data and security teams distinguish normal activity from meaningful risk. Attendees will learn to set behavioral baselines, spot unusual access patterns, use risk scoring, link activity across systems, and prioritize events that need investigation.

The session will use realistic scenarios with SQL Server, cloud databases, Snowflake, Databricks, data lakes, and analytics platforms to examine suspicious activities, including unexpected after-hours access, unusual query volumes, access to sensitive records outside a user’s normal responsibilities, bulk data downloads, and changes in privileged-account behavior.

The session will also present a repeatable approach for tuning monitoring policies, reducing false positives, improving alert quality, and connecting behavioral detections to an effective incident-response process. Attendees will leave with a practical framework and checklist for building more intelligent, risk-based monitoring across modern data environments.