Why AI agent security starts where it runs, not how it behaves 🔐

Most teams run AI agents directly on local developer laptops without realization of the security risks. Combining untrusted data access, side effects, and local credentials creates a dangerous setup with zero central visibility or audit logs. The fix: Shift from probabilistic guardrails to system level guarantees. Run agents inside isolated, ephemeral cloud sandboxes that reset after each run. Enforce least privilege access using scoped credentials and read-only tokens. Add explicit human approval steps for actions with persistent effects. By containing the blast radius at the infrastructure level, you protect sensitive credentials even if a model hallucinates or faces a prompt injection attack. Check out the full article to dive deeper into building safe agent infrastructure: Agent security starts with where the agent runs, not how it behaves Most engineering teams evaluate agent security by asking how the model behaves. Patrick Dawkins, Principal Software Engineer at Upsun, argues that it is the wrong question. The risk starts with where the agent runs and what it can reach from there. upsun.com