AI guardrails

AI guardrails are enforced checkpoints, not just instructions written into a prompt. On the input side, a detector screens a message before it reaches a model, for example for personal data or an attempt to override the system prompt. On the output side, a validator checks that what the model produced is well-formed before it is used, for example that it is valid JSON or matches an allowed set of values.

Prompt instructions describe what a model should do, but nothing stops a model from ignoring them under the right pressure. A regulated enterprise needs checkpoints that hold regardless of what the model decides, and an auditable record of every check that ran.

Before an agent's drafted email goes out, a validator confirms the message is well-formed and a detector confirms nothing in the conversation triggered an injection attempt, before the send step ever runs.

In Dynamiq, guardrails are ordinary workflow nodes: detectors for personal data, prompt injection and policy violations, and validators for JSON, Python, allowed choices and regular expressions, placed anywhere in a workflow and branched on with a choice node. Every verdict is recorded in the run's trace, and a flagged branch can route to a refusal or to a person for review.