Secure AI Agents in Azure: AI Gateway, Tools, and Trust

Microsoft Developer covers how to secure AI agents running on Azure, focusing on trust boundaries, safe tool access, and gateway-based controls to reduce common agent risks like prompt injection and data leakage.

Overview

The video focuses on security considerations for building and operating AI agents on Azure, with emphasis on controlling how agents access tools and external systems, and how to establish trust and guardrails around agent behavior.

Core themes

• Securing agent-to-tool interactions (what the agent is allowed to call, and under what conditions)
• Using a gateway approach to enforce policy and reduce risk when agents interact with external APIs and services
• Threats commonly associated with agentic systems, including prompt injection and unintended data exposure

What “trust” means for AI agents

• Establishing clear trust boundaries between:
  • The model/agent runtime
  • Tooling and connectors (APIs, plugins, internal services)
  • Data sources and downstream systems
• Treating tool access as a privileged capability that needs explicit controls and auditing

Security controls discussed at a high level

• Gateway-based enforcement for agent traffic to tools/APIs
• Identity and access controls for tool execution
• Guardrails to limit what an agent can do and what data it can access

Related Azure services and concepts mentioned

• Azure (agent hosting and platform context)
• Azure AI services and agent tooling (Azure AI Foundry / Agent Service terminology implied by the topic)
• AI gateway pattern for policy enforcement and risk reduction