Safe data discovery with EDB's Data Governance Co-Pilot AI quickstart

Safe data discovery with EDB's Data Governance Co-Pilot AI quickstart Retrieval-augmented generation (RAG) with privacy Governance policies as filters An air-gapped cloud native stack "Restricted Mode" by default Fast time-to-insight Final takeaway Red Hat AI About the authors Giri Venkataraman Shane Heroux Bilge Ince Peter Samouelian More like this Enable intelligent insights with Red Hat Satellite MCP Server AI quickstart: Protecting inference with F5 Distributed Cloud and Red Hat AI Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share When Red Hat revealed our AI quickstarts , EDB suggested a use case to balance the business need for data with the non-negotiable demand for governance. We often treat this as a zero-sum game, but what if the architecture itself could negotiate peace? This Data Governance Co-Pilot AI quickstart , built on Red Hat OpenShift AI and EDB Postgres AI (PGAI) platform, treats safe data discovery as a requirement. It provides a protected workspace where any data consumer can navigate complex schemas and extract insights with less risk of tripping compliance wires. The real governance challenge with agentic AI isn't just what the large language model (LLM) knows, it's what it can do. Agents with direct database access can execute queries autonomously, bypass access controls, and return raw personally identifiable information (PII) to the chat interface. The architecture needs to make compliant behavior the only possible behavior. The AI quickstart is built on pg-airman-mcp , EDB's open source Model Context Protocol (MCP) server for Postgres integrated with PGAI. It uses agentic tool calling to Postgres for an innovative safeguard—an analytics tool that merges static data governance policies with a natural language query interface. This means that policies concerning data privacy, confidentiality, and data timeliness become an active filter directly embedded within the analyst's tooling and workflow. User queries are processed through an uploaded governance policy and the LLM to generate policy-compliant SQL statements, preventing data leakage to the chat interface. By handling masking at the SQL level, this prevents the LLM from accessing the raw data payload. Usually, data governance policies are static documents.