Captured before automated identity checks (Oct 14, 2026): accurate as of its capture on Jul 11, 2026; our capture on Oct 17, 2026 found the same text, but its identity was not checked by them.
This page describes what the document states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability may vary by jurisdiction. Methodology
“We also provide customers with a clear disclosure of terms of use and the intended applications of Salesforce's AI capability.”
“We enable customers to remain in control of their data and models at all times. The data we manage does not belong to Salesforce—it belongs to the customer.”
“We expect our customers to use our AI responsibly, and in compliance with their agreements with us, including our Acceptable Use Policy.”
“data masking is the process that replaces sensitive Personally Identifiable Information (PII) or proprietary business data with non-identifiable tokens before the prompt is sent to the LLM.”
“Should harmful content be identified, the system's content filtering will automatically filter, block, or flag the output before it is displayed to the end-user...”
“We adhere to the highest security and privacy practices to help anticipate and mitigate unintended harm and keep our products safe.”
This document sets out what Salesforce Einstein promises about how it handles your data and AI outputs, and what it expects from you in return. Your data belongs to you, not Salesforce — prompts and AI responses are never stored or used to train underlying AI models, and sensitive information is masked before it reaches those models. In exchange, you are expected to use Salesforce Einstein responsibly and in accordance with its Acceptable Use Policy.
The Salesforce Trusted AI Principles establish Salesforce Einstein's substantive commitments regarding data ownership, AI transparency, content safety, and acceptable use. Customers retain exclusive ownership and control of their data at all times, with a zero data retention policy prohibiting storage or use of prompts and responses to train third-party large language models. Salesforce Einstein applies data masking at the prompt stage to de-identify sensitive PII and proprietary data before it reaches the underlying LLM, and operates real-time toxicity detection and automated content filtering for harmful outputs. The document places affirmative obligations on customers to use AI responsibly and in compliance with Salesforce's Acceptable Use Policy, while Salesforce Einstein commits to model explainability at the point of prediction, public model cards, and bias-mitigation tools for employees, customers, and partners.
As an individual user, your data and prompts remain your property and are never stored or used to train the underlying large language models. Sensitive personal information in prompts is automatically replaced with non-identifiable tokens before reaching the model, and any harmful content identified in AI outputs is filtered or blocked before you see it. Salesforce Einstein also provides an explanation of how each AI prediction or recommendation was reached at the time it is delivered, and publishes public model cards describing each model's design, intended and unintended uses, known ethical implications, and performance — resources you can consult to understand the AI systems you are using.
Which mapped governance frameworks each document engages, tied to the specific provisions that engage them.
We withheld clauses we had published from this document. Each withholding is recorded in our correction log:
“We also provide customers with a clear disclosure of terms of use and the intended applications of Salesforce's AI capability.”
“We enable customers to remain in control of their data and models at all times. The data we manage does not belong to Salesforce—it belongs to the customer.”
“We expect our customers to use our AI responsibly, and in compliance with their agreements with us, including our Acceptable Use Policy.”
“data masking is the process that replaces sensitive Personally Identifiable Information (PII) or proprietary business data with non-identifiable tokens before the prompt is sent to the LLM.”
“Should harmful content be identified, the system's content filtering will automatically filter, block, or flag the output before it is displayed to the end-user...”
“We adhere to the highest security and privacy practices to help anticipate and mitigate unintended harm and keep our products safe.”
“We comply with applicable laws governing AI research and use.”
“Toxicity detection employs advanced classification models to scan and categorize generated content in real time for signs of hate speech, bias, harassment, or other policy violations.”
“Zero data retention is a strict policy where the prompts and generated responses are never stored or used to train the underlying third-party large language models, guaranteeing the data remains exclusively the customer's property.”
“We also provide model explainability when an AI prediction or recommendation is made.”
“We publish model cards that describe how models were created, intended and unintended use cases, known ethical or societal implications, and performance scores.”
“We will do this by creating tools and resources to help Salesforce employees, customers, and partners identify and mitigate bias in the systems that they are building and using...”
Every distinct legal provision identified in this document. Featured provisions appear above with analysis.
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