What you'll learn

AI agents are increasingly moving beyond generating text and are being given access to tools that can read data, modify systems, and perform actions on behalf of users. These tool interfaces create new security boundaries where poor design, excessive permissions, or misplaced trust can result in unintended or malicious outcomes. In this course, Agentic AI Security: Tool Interfaces and Least Privilege, you’ll gain the ability to design and secure agentic AI systems by applying proven security principles to tool usage, permissions, and workflows. First, you’ll explore the security risks introduced by agent tool interfaces, including trust boundaries, attack surfaces, and how traditional application vulnerabilities apply to agentic systems. Next, you’ll discover defensive design patterns including least privilege, scoped tools, authorization controls, validation, and workflow-based safeguards. Finally, you’ll learn how to apply these principles to MCP-based integrations, securing agent ecosystems where tools, resources, and external services may introduce additional trust risks. When you’re finished with this course, you’ll have the skills and knowledge of agentic AI security needed to design, evaluate, and implement AI agents that can safely interact with real-world systems.