Guardrails
Definition
Guardrails are the technical limits around an AI system: allowed tools, value ranges, spending and volume caps, forbidden actions and mandatory approvals. They turn an unpredictable model into a system that fails safely and visibly.
Effective guardrails are enforced outside the model — in code, in the integration layer, in database constraints — so a persuasive but wrong model output still cannot cause damage.
They also make operations auditable: every blocked action is logged, and every threshold has an owner who can change it deliberately.
In practice
- Hard limits on amounts, volumes, recipients and destructive actions
- Enforced in code, not by asking the model to behave
- Every block and override is logged for review
Related terms
- AI governanceAI governance is the set of rules defining which AI systems a company operates, on what data, with which permissions, under whose responsibility, and how outcomes are reviewed. It makes AI use auditable towards management, auditors and regulators.
- Human in the loopHuman in the loop means a defined point at which a person reviews, approves or corrects the AI system's work. The handover is designed in advance for specific triggers — value thresholds, low confidence, unknown counterparties — rather than left to chance.
- HallucinationA hallucination is a confident but false output from a language model — an invented figure, clause or reference. It is a property of statistical text generation, which is why production systems verify outputs against source data instead of trusting them.