Deploy AI with confidence. Prove it with logs.
Every interaction is governed as it happens and logged from prompt to source and back.
Open your data to AI. Keep control of every request
Policy, permissions, audit logs, and a kill switch for every AI tool you connect.
“Sales reps can read Salesforce accounts and pipeline, but never salary or comp fields. Block deletes for everyone.”
Will this work in my environment?
Access control, audit, and observability that plug into the identity providers and security tooling your teams already run.
Access control models
- RBAC
- ABAC
- Time-windowed access
- Custom business rules
- Agent service accounts
Identity providers
- Okta
- Azure AD
- Ping Identity
Audit destinations
- Query-level logs
- SIEM-ready export
- Real-time dashboard
Deployment
- Cloud
- Hybrid
- On-prem sources
Most tools govern at one layer. We govern at two.
At the AI layer, set what each tool can access and act on. At the connectivity layer, set which systems it can reach.
Route and govern AI traffic
A combined LLM, MCP, and agent gateway routes every request to an approved model under policy.
Runtime controls on what each agent may call and do, enforced on every request, not just at provisioning.
Budgets, rate limits, and spend attribution per team or agent.
Every request traced from prompt to model to answer, visible from one control plane.
Claude
GPT
GeminiGoverned work in live systems
System roles define platform capabilities; custom access roles bundle connection and workspace permissions, assigned in bulk.
Groups sync from your identity provider automatically. Members inherit assigned roles with no manual provisioning.
SELECT, INSERT, UPDATE, DELETE, and EXECUTE per user per connection. Additive across roles, no conflicting models.
Hundreds of first-party connectors doing governed reads and writes in your live systems, with no stale copies.
Salesforce
Snowflake
SAPAnswer any question about your AI in minutes.
Who did what, why it happened, and how it's performing. Captured automatically and ready wherever your team already works.
Reconstruct what changed
Capture login, permission, role, connection, administrative, and data-access activity in a structured audit trail.
Keep identity attached
Associate activity with the Connect AI user responsible, including when downstream systems use shared credentials.
See AI activity together
Review model requests and MCP tool activity alongside data and administrative events.
Investigate performance
Use request, session, latency, status, token, model, and connection context to troubleshoot behavior.
Fit existing workflows
Forward structured Connect AI events into a SIEM, like Datadog or Splunk, instead of creating a separate monitoring silo.
Use the telemetry elsewhere
Pull observability data programmatically for dashboards, investigations, reporting, or your own operational processes.
Controlled, logged, and observable, from end-to-end
Three checks run against every AI-to-data interaction with every step logged and auditable.
Director of Business Systems
Get AI under control before it becomes a governance problem.
Talk to our team about access controls, audit requirements, and observability for your AI or try it for yourself today.