Zero-Trust GenAI: Securing Tool-Enabled LLM Workflows in the Enterprise

This article explores how tool-enabled LLM systems expand the risk surface by introducing real-world actions into AI workflows. It argues that zero-trust architecture is essential for securing these systems, shifting trust away from the model and distributing control across pre-, in-, and post-execution layers. By enforcing strict boundaries, validating outputs, and ensuring full observability, organizations can safely scale agentic AI without exposing themselves to unintended actions or data leaks.