Data security and usage audit
Bring data policy into every model interaction
Apply detection, masking, blocking, alerting, and traceability controls according to the enterprise environment.
Global models
Local models
Written policy alone does not control model usage
Sensitive input exposure
Contracts, customer data, source code, or internal information may reach models without warning.
Inconsistent tool boundaries
Employees use different tools and personal accounts outside a common control plane.
Insufficient audit evidence
After an incident, it is difficult to reconstruct who used which data and model.
Connect policy, detection, response, and traceability
AIPay configures controls around the organization’s data classification and workflows. Detection coverage and retention depend on the selected architecture.
Policy configuration
Define allow, warn, mask, block, and human-review actions.
Sensitive-data detection
Configure detection by data type, team, and use case.
Incident response
Track alerts, ownership, rationale, and resolution status.
Usage audit
Record identity, model, policy events, and necessary call context.
Typical deliverables
- Data classification and usage boundaries
- Security policy and response workflow
- Audit fields and retention recommendations
- Administrator and employee enablement
Who this is for
- Teams handling customer or business-sensitive data
- Security leaders requiring AI usage auditability
- Enterprises expanding employee AI access
Next step