Introduction

  • 3 minutes

Artificial intelligence is evolving rapidly, and one of the most significant shifts is the rise of AI agents. An AI agent is a software system that uses data, tools, and assigned permissions to work toward a defined goal.

Unlike AI experiences that primarily generate responses, AI agents are designed to take action. Depending on the scenario, they might retrieve data, invoke tools and services, and complete one or more tasks on behalf of users or organizations.

For example, an AI agent might gather customer information, draft a follow-up email, or update a customer record after a meeting.

As AI systems become more capable, they also introduce new security challenges. When an agent can act across systems, access sensitive data, and automate processes, organizations must ensure these actions occur safely and within defined boundaries.

Photograph of a developer working at a desk with multiple monitors.

This module explores how to securely deploy AI agents and workloads in modern development. You learn how identity, governance, policy controls, and containment strategies help protect AI-powered applications while enabling agents to operate safely.

Windows includes platform security capabilities that can help developers apply identity, policy, monitoring, and containment practices as they build, deploy, and run AI-powered workloads. Understanding these foundational security concepts helps you apply them throughout the AI development lifecycle.

Imagine an AI agent that helps an employee prepare for a customer meeting. The agent might retrieve customer information, summarize relevant documents, and draft a follow-up email. To support this workflow safely, organizations need controls that define what the agent can access, what actions it can perform, and when additional approval is required.

Learning objectives

By the end of this module, you can:

  • Describe how AI agents differ from AI experiences that primarily generate responses.
  • Identify key security risks introduced by agent-based workflows.
  • Explain how identity, governance, and policy controls help govern agent behavior.
  • Describe how containment strategies help reduce risk while AI agents are running.
  • Explain how security practices protect code, AI agents, models, and data throughout the AI development lifecycle.