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This provision describes a technical architecture that connects the LLM to customer enterprise data sources, restricting model outputs to customer-approved sources to reduce hallucinations while preserving existing data access permissions and controls.
This analysis describes what Salesforce Einstein's agreement states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability and practical outcomes may vary by jurisdiction, enforcement context, and individual circumstances. Read our methodology
This provision establishes that AI outputs within the Salesforce platform are grounded in customer-approved data sources and that existing data access controls are maintained during AI processing, which has direct implications for data segregation, access control compliance, and the accuracy of AI-generated outputs used in business decisions. The maintenance of permissions during AI data retrieval is relevant to compliance with data access governance frameworks and insider threat controls.
Interpretive note: The document does not specify the full scope of data sources and access control frameworks covered by the dynamic grounding architecture, creating uncertainty about its adequacy for customers with complex identity and access management environments.
Under this provision, AI outputs are grounded in customer-approved enterprise data sources and existing data access permissions are preserved during AI data retrieval. Enterprise customers should assess whether this architecture satisfies their specific data access governance and segregation requirements for AI-assisted workflows.
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"Dynamic grounding securely connects the LLM to your enterprise data (such as information stored in Data 360 or internal knowledge bases) and dynamically retrieves the validated information. This prevents the LLM from producing 'hallucinations' by forcing the model to cite and use trusted, customer-approved sources. Secure data retrieval allows users to securely access the data to ground generative AI prompts in context about your business while maintaining permissions and data access controls.Excerpt from Salesforce Einstein's Salesforce Trusted AI Principles
(1) REGULATORY LANDSCAPE: This provision engages GDPR data minimization and access control requirements, HIPAA technical safeguard requirements for access controls over electronic protected health information, and SOC 2 access control standards for enterprise SaaS platforms. The maintenance of existing permissions during AI data retrieval is relevant to NIST Cybersecurity Framework access management controls. (2) GOVERNANCE EXPOSURE: Low. The provision describes a technically meaningful access control mechanism. Governance exposure relates primarily to whether the permissions maintenance mechanism covers all relevant data categories and access control frameworks in use by the customer. (3) JURISDICTION FLAGS: EU and EEA deployments should assess whether the dynamic grounding architecture satisfies GDPR requirements for data access controls and purpose limitation. Healthcare customers must evaluate whether permissions maintenance during AI retrieval satisfies HIPAA minimum necessary access requirements. (4) CONTRACT AND VENDOR IMPLICATIONS: Procurement teams should request technical documentation on the permissions enforcement mechanism to verify it covers all relevant identity and access management integrations. The reference to specific data sources (Data 360, internal knowledge bases) suggests the architecture may have scope limitations relevant to customers using other data sources. (5) COMPLIANCE CONSIDERATIONS: Data governance teams should map which enterprise data sources are covered by the dynamic grounding architecture, assess whether existing access control configurations are correctly inherited by AI processing workflows, and identify any data categories that may require additional access controls beyond those described.
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This provision establishes that AI outputs within the Salesforce platform are grounded in customer-approved data sources and that existing data access controls are maintained during AI processing, which has direct implications for data segregation, access control compliance, and the accuracy of AI-generated outputs used in business decisions. The maintenance of permissions during AI data retrieval is relevant to compliance with …
Under this provision, AI outputs are grounded in customer-approved enterprise data sources and existing data access permissions are preserved during AI data retrieval. Enterprise customers should assess whether this architecture satisfies their specific data access governance and segregation requirements for AI-assisted workflows.
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