Agentic AI systems retrieve data, invoke tools, and execute actions across enterprise environments. Yet most agents still lack built-in controls. Without clear security boundaries, an agent can expose sensitive data in seconds, trigger workflows it should never touch, or disrupt critical business operations.
As a result, enforcing guardrails across the entire AI flow and centrally managing access for all identities, both human and machine, has become a critical business priority.
This session reviews common failure patterns in agentic AI environments and examines how dynamic controls can be applied across prompts, data access, tool usage, and outputs. It shows how these controls can be enforced in a distributed manner across the enterprise technology stack, enabling organizations to deploy agentic AI at scale while protecting sensitive information and maintaining operational control.
