The Salesforce Einstein Trust Layer for Retrieval-Augmented Generation (RAG) for Enterprise Applications
This paper discusses how the Einstein Trust Layer facilitates the safe practical application of RAG in enterprise systems, including its general architecture, functionality, and the precise processes that demonstrate why the Einstein Trust Layer is a reliable means of incorporating LLMs into commercial processes.
Abstract
Generative AI has the potential to revolutionize the enterprise workflows to great extent but it poses privacy, security, and data governance challenges. Companies who want to utilize advanced AI models like Large Language Models, are only allowed to do so under the guidelines of security and regulatory frameworks. Salesforce Einstein Trust Layer proposes a solution to these challenges by not only setting up a trusted layer for deploying Retrieval-Augmented Generation (RAG) models but also ensures that the data privacy standards are met while delivering the AI generated responses. This paper discusses how the Einstein Trust Layer facilitates the safe practical application of RAG in enterprise systems, including its general architecture, functionality, and the precise processes that demonstrate why the Einstein Trust Layer is a reliable means of incorporating LLMs into commercial processes. Keywords: Salesforce Einstein Trust Layer, Retrieval-Augmented Generation (RAG), Generative AI, Large Language Models (LLMs), Data Privacy, AI Governance, Enterprise AI, Data Masking, Toxicity Scoring, Dynamic Grounding, Zero-Data Retention, AI Compliance