Traditional data loss prevention fails when applied to AI conversations. Organizations discover shadow AI usage exceeds approved tools by three to five times, creating blind spots where intellectual property flows undetected. Legacy DLP systems scanning for keywords miss contextual data exfiltration - clinical trial data shared without containing flagged terms like "confidential."

The NIST AI Risk Management Framework requires understanding user intent rather than pattern matching. Effective governance demands network-level visibility, real-time intent classification and nuanced controls that redirect sensitive queries to internal models while maintaining productivity.

In this insightful discussion, Sharat Ganesh, head of product marketing at WitnessAI, will explore practical strategies for:

• Building intent-based classification systems that understand context and purpose;
• Implementing NIST-aligned governance frameworks for dynamic risk management;
• Establishing comprehensive visibility into fragmented AI usage across enterprise applications.