Setting Boundaries for Agentic AI: Preventing Data Exposure at Enterprise Scale

About this session

Agentic AI systems retrieve data directly or via MCP tools, yet most lack built-in controls. Without clear security boundaries, they can expose sensitive data and disrupt operations within seconds. As a result, enforcing guardrails across the entire AI flow and centrally managing access for all identities, human and machine, has become critical. This session explores how runtime controls are applied and enforced across distributed environments to enable secure, scalable agentic AI.