Securing RAG Pipelines in Enterprise SaaS Platforms
🔒 Enterprise SaaS products increasingly adopt Retrieval-Augmented Generation (RAG) to give AI agents access to customer-specific knowledge, but that bridge also creates severe security liabilities. The article reviews recent high-profile failures — from the EchoLeak zero-click exfiltration to vector database reconstructions, indirect prompt injections in IDEs and large-scale knowledge-base poisoning — and breaks down the typical three-phase RAG architecture: ingestion & embedding, vector storage & retrieval, and LLM generation. It advocates a defense-in-depth posture combining pre-ingest DLP, retrieval-time RBAC/ABAC, prompt isolation and output filtering, and highlights Google Cloud services like Cloud DLP, Vertex AI vector search, Vertex AI model armor and Security Command Center to operationalize those controls.