How to secure AI agents in the cloud

AI agent security in the cloud requires a dual-layer approach, focusing on both the model and infrastructure layers to mitigate unique risks associated with AI's dynamic task execution. While model-layer controls like prompt filtering and output validation aim to reduce unintended actions, the infrastructure layer is essential for limiting the capabilities of compromised agents through execution isolation, role-based access control (RBAC), secrets management, audit logging, and network isolation. Northflank provides comprehensive infrastructure controls by default, using microVM sandbox isolation to prevent compromised agents from affecting adjacent workloads, managing credentials outside of code or logs, and enforcing RBAC to ensure agents operate with the least privilege necessary. This approach also incorporates audit logging for forensic investigations and network policies to restrict unauthorized communications. In addition, Northflank supports the "Bring Your Own Cloud" (BYOC) model, allowing sensitive data processing within an enterprise's own cloud account for compliance and enhanced security.

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