Designing AI workflows: principles for safety and control

Adopting AI in workflows requires understanding that large language models (LLMs) do not operate like traditional software, as their outputs can vary and be prone to errors or manipulation. Recognizing this, the text explores several principles for building secure AI workflows, focusing on separating trusted instructions from untrusted input, setting deterministic boundaries on tool access, applying minimal privilege to model-driven actions, incorporating human oversight at critical points, and assuming prompt injection will occur. Using Tines as an AI interaction layer facilitates implementing these principles by allowing builders to define and separate system instructions from user inputs, enforce restricted tool access, and maintain human oversight through native action types. While no single measure can eliminate risks like prompt injection, the combined approach ensures AI actions remain within a controlled and auditable framework, allowing for secure and reliable AI-powered workflows.