Guardrails

Policies and technical controls that constrain what an AI can say or do, preventing harmful or out-of-scope behavior.

When to use it

  • Any feature handling user-generated content or sensitive data.
  • Enterprise deals that require explicit safety controls.
  • High-risk tools (payments, PII) exposed to models.

PM decision impact

Guardrails reduce legal and brand risk but can increase refusals and latency. PMs decide which risks matter most, where to enforce (pre/post), and how to explain refusals in UX. Strong guardrails enable faster approvals and broader rollout.

How to do it in 2026

Layer controls: input filters, prompt rules, model safety settings, output classifiers, and tool allowlists. Log everything. In 2026, tie guardrails to intents and user tiers (stricter for unknown users) and run red-team checks continuously.

Example

Adding layered guardrails to a sales copilot cut unsafe outputs to <0.2% while keeping conversion impact neutral. Refusal messaging clarified scope, keeping CSAT steady at 4.6/5.

Common mistakes

  • Using one-size-fits-all refusals that block legitimate requests.
  • Relying only on provider safety toggles without your own checks.
  • Not auditing guardrail effectiveness over time.

Related terms

Learn it in CraftUp

Last updated: February 2, 2026