Architecture / AIGIS resources
Once data is in prompt context, the model can reason from it. Runtime governance needs to shape context before the model receives it.
Executive read
Prompt construction is a security boundary.
Permissions should be checked before model exposure.
Excluded data should be recorded as evidence.
Model routing should happen after governance, not before.
Analysis
Output filters can catch some issues after generation, but they cannot prove forbidden context was absent.
AIGIS focuses on the earlier boundary: the moment enterprise data is selected, filtered, and assembled for a model or non-model execution path.
Ask whether the runtime can explain why a record was included, why a field was excluded, and which model received the final context.
If that answer is not inspectable, the governance story is incomplete.
Resource packet
Evidence packet
Capture user context, system of record, enforcement tier, stripped fields, model route, response, hash marker, and fallback notes.
Salesforce is the production proof path. ServiceNow and SAP are design-partner co-development paths with asymmetric enforcement that must be disclosed in diligence.
Keep reading
Architecture
A practical architecture for enterprise AI governance middleware: identity resolution, live record checks, field stripping, model routing, write approvals, and audit provenance.
Audit
What audit teams should expect from AI permission-provenance evidence across Salesforce-first governed workflows.
Architecture
Why enterprises should keep permission enforcement stable while model routing remains flexible.