How do you implement RAG with guardrails for enterprise compliance?

Enterprise RAG guardrails ensure outputs comply with organizational policies, regulations, and quality standards. Input guardrails validate queries before processing, blocking off-topic requests, detecting prompt injection attempts, and enforcing usage policies. Retrieval guardrails enforce document-level access control, content classification restrictions, and data handling compliance. Output guardrails check responses for PII leakage using regex and NER models, verify factual grounding against retrieved sources, enforce brand voice and tone guidelines, and filter inappropriate or non-compliant content. NVIDIA NeMo Guardrails provides a framework for implementing conversational guardrails. Guardrails AI offers a Python framework for output validation with validators for factuality, toxicity, PII, and custom business rules. For regulated industries, implement audit logging of all queries, retrieved documents, and generated responses with retention policies. Test guardrails with adversarial inputs to ensure robustness against circumvention attempts.