Secure AI agents with Policy in Amazon Bedrock AgentCore

Deploying AI agents safely in regulated industries is challenging. Without proper boundaries, agents that access sensitive data or execute transactions can pose significant security risks. Unlike traditional software, an AI agent chooses actions to achieve a goal by invoking tools, accessing data, and adapting its reasoning using data from its environment and users. This autonomy is precisely what makes agents so powerful and what makes security a non-negotiable concern.

A useful mental model for agent safety is to isolate the agent from the outside world. To do this, think of walls around an agent that defines what the agent can access, what it can interact with, and what effects it can have on the outside world. Without a well-defined wall, an agent that can send emails, query databases, execute code, or trigger financial transactions is risky. These capabilities can lead to data exfiltration, unintended access to code or infrastructure, or ...