As employees bypass traditional security perimeters to use unapproved LLMs, autonomous agents and AI tools, organizations face severe risks, from exposed proprietary code and personally identifiable information (PII) to new system attack vectors.
This session explores how to eliminate critical visibility gaps using AI-aware data leakage prevention (DLP) and automated sanitization protocols to protect sensitive data before external transmission. Speakers will also discuss practical governance methodologies – including Glorin Sebastian's Digital Shadow AI Risk Theoretical Framework (DART) – to help identify behavioral vulnerabilities and data sovereignty conflicts.
Finally, we will address urgent global compliance requirements. With the EU AI Act in full effect alongside the GDPR, the California Consumer Privacy Act and evolving data privacy laws, attendees will learn how continuous shadow AI discovery prevents unauthorized cross-border data flows and costly regulatory penalties.
Attendees will leave with actionable strategies to:
- Audit AI behavior with established frameworks: Utilize risk methodologies to evaluate unintentional data disclosure and mitigate reliance on unvetted workflows
- Modernize DLP and sanitization: Deploy AI-aware data leakage prevention and automated sanitization to strip sensitive IP and PII prior to external exposure
- Enforce global regulatory compliance: Implement continuous discovery and auditing to prevent unauthorized data processing and avoid severe regulatory fines
