Guardrails harness
Guardrails sit between a model and the people who use it, checking what goes in and what comes back out, so a single prompt or a single bad response can't become an incident.
- Policy harness
- Model routing harness
- Data transfer harness
- Guardrails harness
- Security harness
- Evaluation harness
- Observability harness
- Cost and token harness
- Reliability harness
- Audit harness
What it controls
Guardrails inspect every input before it reaches a model and every output before it reaches a person. On the way in, that means catching prompt injection attempts, flagging requests outside what a model is meant to be used for, and stripping or masking personal data that shouldn't be sent to a model at all. On the way out, it means filtering unsafe content, catching off-policy answers, and redacting anything the responder isn't cleared to see.
Why it matters
A model answers whatever it's asked and returns whatever it generates. It has no sense of your company's policy, your customers' privacy expectations, or what a malicious prompt looks like unless something is built to check for that first. Without guardrails, a single crafted input can extract data it shouldn't, or a single ungoverned output can leak something sensitive. That's not the model failing; it's a harness that was never built.
What good looks like
- Every input screened for injection attempts and policy violations before it reaches a model
- Personal and sensitive data masked or redacted automatically, not left to the requester's judgment
- Every output checked against policy before it's shown to a person
- Clear, auditable rules for what gets blocked, what gets escalated, and what passes through
How it fits with the other harnesses
Guardrails decide what a model is allowed to see and say in the moment. Policy decides who gets to ask in the first place, and Audit keeps the record of what happened. Guardrails is the layer that actually intervenes, request by request, between those two.
In practice
Real patterns on the Resources page that lean on this harness:
Frequently asked questions
What's the difference between Guardrails and Policy?
Policy decides who can use which model with which data, set once and enforced on every request. Guardrails checks the content of each individual request and response against that policy in real time.
Does this slow down every request?
A guardrail check runs on every input and output, but it's designed to be a fast pass, not a bottleneck. Most traffic passes through without a human ever seeing it; only flagged content gets escalated.
What happens to a blocked request?
A blocked or flagged input or output doesn't reach the other side silently. It's logged and, depending on policy, either rejected outright or escalated for a human to review.
Questions about your AI rollout?
Talk to us about which harnesses fit your models and data.