Guardrails
Guardrails are runtime checks placed around an AI system to constrain inputs, outputs, and tool calls within safety, compliance, and business policy.
What is Guardrails?
Guardrails sit before and after the model. Pre-checks include prompt injection detection, PII redaction, topic allow-lists, and rate limits. Post-checks include schema validation, profanity and toxicity classifiers, citation verification, fact-grounding checks, and tool-call allow-lists. Guardrails can be implemented as classifiers, regex rules, validation libraries, or LLM-based judges. They are most effective when combined.
How does Guardrails apply to enterprise AI?
Enterprise AI without guardrails is uninsurable. The EU AI Act, GDPR, and sectoral rules effectively require pre-deployment risk controls, including filters on personal data leaving the organisation.
Related terms
- Hallucination - A hallucination is a confident-sounding output from a generative AI model that is not grounded in any source and is factually wrong.
- AI Risk Management - AI risk management is the discipline of identifying, assessing, mitigating, and monitoring the harms an AI system can cause across its lifecycle.
- Transparency Notice - A transparency notice is a clear disclosure to users that they are interacting with an AI system, what it is doing with their data, and what its limits are.
- AI Audit Trail - An AI audit trail is the persistent, tamper-evident record of every input, output, tool call, model version, and decision an AI system has made, sufficient to reconstruct any past interaction.
External references
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