AI guardrails are safety and quality controls around model behavior. They can include system instructions, content filters, validation rules, retrieval limits, tool permissions, and human approval steps.
In practice
Guardrails help keep an AI product aligned with policy, brand, privacy, and user expectations. They are especially important when the AI can access private data or take actions.
What to watch
Guardrails should be tested against real failures. A guardrail that looks good in a demo may fail under messy user inputs or malicious documents.