Use case / AIGIS resources
Salesforce data leakage is not only a database problem. It becomes an AI problem the moment CRM fields, records, or redacted placeholders enter a model prompt. AIGIS prevents leakage by removing inaccessible data before model context exists.
Executive read
AI data leakage prevention starts before prompt construction.
Salesforce fields the user cannot read should be stripped, not masked.
Record-level access should be checked live because visibility changes.
The audit trail should explain which Salesforce data was included or denied.
Analysis
A Salesforce report or UI can enforce permissions at render time. An LLM prompt is different: once the context is assembled, the model can use every token it sees.
AIGIS treats prompt construction as the critical control point. Data that fails Salesforce object, record, or field checks is not included.
Sending a redacted field name can still leak that the field exists. In CRM, the existence of a field can reveal sensitive operational facts about scoring, segmentation, renewal risk, compensation, or legal review.
AIGIS strips inaccessible fields entirely so the model cannot infer from their names.
Security teams need evidence, not trust. AIGIS records which Salesforce objects and records were checked, which fields were included, and which fields were stripped.
That creates an audit receipt for each answer.
Comparison
Keep reading
Use case
How AIGIS governs Salesforce AI access with object, record, and field permissions while routing safely across SAP, ServiceNow, databases, and other enterprise systems.
Architecture
A practical guide to LLM field-level security: why prompt masking leaks structure, why field stripping works better, and how AIGIS enforces access before model context.
Comparison
Compare AIGIS with Salesforce Agentforce for cross-system AI governance, field-level permissions, model choice, deployment model, and enterprise stack cost.