W-22960628 Add AI Features and Data Usage page#456
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Jun 12, 2026
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| This feature uses Java thread dumps from your deployed applications and the Diagnostic Information Analysis File (DIAF), which includes application metadata, performance statistics, error messages and stack traces, and recent log entries. It also uses CloudHub or CloudHub 2.0 instance information and deployment configuration. | ||
| | Google Gemini 2.5 Flash | ||
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Can we add a See Also?
== See also
- xref:usage-reports.adoc[]
- xref:pricing.adoc[]
- xref:usage-metrics.adoc[]
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| = AI Features and Data Usage | |||
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| AI in MuleSoft Anypoint Platform is built on data. Before you use a feature with AI in MuleSoft, it is important to understand what data is used and how. | |||
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Suggestion for intro:
MuleSoft AI features use your data to generate responses. Understanding what data each feature uses helps you make informed decisions about privacy, compliance, and how you work with AI in MuleSoft.
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| AI in MuleSoft Anypoint Platform is built on data. Before you use a feature with AI in MuleSoft, it is important to understand what data is used and how. | ||
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| All MuleSoft AI features access large language models (LLMs) within the Salesforce Trust Boundary. Data that you submit to MuleSoft AI features is processed securely within Salesforce-managed boundaries and routed through your connected Salesforce organization for tracking and billing. |
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Suggestion:
All MuleSoft AI features access LLMs within the Salesforce Trust Boundary, a set of security and data governance controls that keeps your data within Salesforce-managed infrastructure. MuleSoft AI features process data you submit securely within Salesforce-managed boundaries and route it through your connected Salesforce organization for tracking and billing.
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| All MuleSoft AI features access large language models (LLMs) within the Salesforce Trust Boundary. Data that you submit to MuleSoft AI features is processed securely within Salesforce-managed boundaries and routed through your connected Salesforce organization for tracking and billing. | ||
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| The following table lists the Customer Data you submit to our services, as defined in our https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/legal/Salesforce_MSA.pdf[Main Services Agreement (MSA)^], and the usage data relating to your interactions with MuleSoft AI features. Customer Data that you submit to MuleSoft AI features is not used to train AI models or shared across customers. |
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Suggested change
| The following table lists the Customer Data you submit to our services, as defined in our https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/legal/Salesforce_MSA.pdf[Main Services Agreement (MSA)^], and the usage data relating to your interactions with MuleSoft AI features. Customer Data that you submit to MuleSoft AI features is not used to train AI models or shared across customers. | |
| This table lists the Customer Data you submit to our services, as defined in our https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/legal/Salesforce_MSA.pdf[Main Services Agreement (MSA)^], and usage data about your interactions with MuleSoft AI features. Customer Data that you submit to MuleSoft AI features is not used to train AI models or shared across customers. |
| | Intelligent Document Processing (IDP) | ||
| | Document Analysis with Custom Schemas | ||
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| This feature uses document images that you upload in base64-encoded format and the extracted text from them, along with natural language prompts and questions that you provide. It also uses document schema metadata that you define, such as field names, descriptions, and data types, and sample data that conveys the expected output format. |
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Suggested change
| This feature uses document images that you upload in base64-encoded format and the extracted text from them, along with natural language prompts and questions that you provide. It also uses document schema metadata that you define, such as field names, descriptions, and data types, and sample data that conveys the expected output format. | |
| This feature uses document images that you upload and the extracted text from them, along with natural language prompts and questions that you provide. It also uses document schema metadata that you define, such as field names, descriptions, and data types, and sample data that conveys the expected output format. |
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