Securing Autonomous AI: Zero Trust, MCP, A2A, and Policy Enforcement

How to build agentic AI systems that can interact with enterprise applications without acquiring unrestricted authority.

Traditional applications execute actions through code paths designed and reviewed by engineers.

Agentic AI introduces a different execution model.

An LLM can interpret a request, choose a tool, construct arguments, and propose an action dynamically.

This flexibility creates powerful opportunities, but it also introduces security challenges.

What happens when an agent selects a tool it should not use?

What happens when a retrieved document contains malicious instructions?

What happens when one agent delegates a task to another agent with broader privileges?

Enterprise AI security must address these questions at the architecture level.

1. Zero trust for AI agents

Zero trust begins with a simple assumption:

No agent, tool, workload, or network segment is inherently trusted.

Every sensitive operation requires authenticated identity and explicit authorization.