Configuring Guardrails

A guardrail is a safety and compliance check applied to data flowing through a Celigo integration. Guardrails are stored as imports with adaptorType: "GuardrailImport" and accessed via the /v1/imports API, but they have a dedicated page in the Celigo UI.

Guardrails handle three concerns:

• Data validation -- check records against rules before they reach downstream systems (PII detection, content moderation, or custom AI-based evaluation)
• Confidence tuning -- control sensitivity via confidenceThreshold (0 to 1, default 0.7). Lower values catch more issues but increase false positives
• PII masking -- optionally return a redacted copy of the record (pii.mask: true) under a masked response field. Masking is NOT automatic -- see PII: mask vs flag

No _connectionId is required unless using BYOK credentials for the ai_agent type. Platform-managed credentials cover most use cases.

Guardrails are used across flows, APIs, and tools.

Guardrail or AI agent step? Pick by what the LLM produces. A guardrail renders a verdict (flagged: true|false) that the parent's routing branches on -- reach for it when the user says "verify / check / validate / flag / screen". An AI agent step does work whose output flows onward as data -- reach for it when the user says "classify / extract / generate / summarize / decide"; build that in configuring-ai-agents.

Guardrails Flag, They Don't Enforce