In the enterprise SaaS space, AI agents are becoming an integral part of the SaaS product. To make these intelligent agents truly useful, they need contextual, customer-specific knowledge, something standard Large Language Models (LLMs), open source or otherwise, inherently lack since they are not trained on customer proprietary data.
Retrieval-Augmented Generation (RAG) is the bridge that grants AI agents real-time access to a company’s most sensitive data: Internal wikis, CRM records, code repositories, task tracking system and intellectual property. However, this bridge introduces significant security liabilities. The cost of getting RAG security wrong in a SaaS environment is catastrophic, ranging from cross-tenant data leaks and unauthorized PII exposure to malicious prompt injections.
Over the past year, several high-profile incidents have underscored the vulnerabilities of enterprise AI integrations:
