Guardrails are policies and controls that prevent AI systems from generating harmful or incorrect outputs.

In conversational analytics, guardrails start with the semantic layer: models are restricted to approved tables, metrics and filters.

Additional measures include validating SQL before execution, limiting free‑text prompts, and applying row‑level security.

You can also implement filters to block sensitive data (e.g., personally identifiable information) from being referenced.

RAG (Retrieval‑Augmented Generation) architectures can ground responses by always citing underlying data.

The Cloudera study reported that 53 % of enterprises rank data privacy as their top concern and 40 % cite integration complexity, reinforcing the need for robust guardrails.