Webinar
Data Governance in the AI Era: Protecting Sensitive Data While Staying Compliant
As AI adoption accelerates, sensitive data no longer stays neatly within controlled repositories. It gets fragmented, transformed, and shared in ways that traditional security models weren't built to handle — creating real challenges for compliance, privacy, and export control programs.
In this session, a Cyberhaven solutions engineer breaks down what a modern data governance strategy looks like in practice.
What you'll learn:
- Why traditional DLP and static classification fall short in an AI-driven environment
- How to gain continuous visibility into where regulated data lives, how it moves, and who touches it
- What "shadow AI" means for your compliance posture and how to apply guardrails without blocking innovation
- How a data lineage approach gives compliance and audit teams the context they actually need
- Strategies for reducing insider risk by combining user behavior, data sensitivity, and context — not just alert volume
What you'll see in action:
- Data Security Posture Management (DSPM) with forward scanning and AI-assisted classification
- Lineage-based DLP that tracks data from origin through every transformation and handoff
- Proportional controls like user coaching that protect data without killing productivity
- Insider risk investigation workflows that surface intent and anomalies, not just volume
- A unified platform integrating DLP, insider risk, AI security, and DSPM in a single view