Responsible AI & Governance
This page explains how umamimind.ai is designed to operate safely in regulated environments: policy enforcement, evidence generation, tenant isolation, and oversight controls.
Model risk & autonomy controls
- Runtime policy gates for every tool/backend action (OPA/Rego).
- Escalation thresholds and approval flows for high-impact actions.
- Budget ceilings and SLA tiers enforced by the router.
- Deterministic replay for post-incident review and audit.
Data handling & boundaries
- Tenant-scoped policies, runs, and evidence artifacts.
- Explicit data boundaries per workflow step (inputs/outputs).
- Redaction and evidence export controls for governance workflows.
- Design supports least-privilege tool access and allowlists.
Security posture (practical)
- CORS restricted to approved origins; secrets stored as environment variables.
- JWT-based access for protected APIs and evidence exports.
- Audit logs persisted in Postgres with immutable run timelines.
- Optional OpenTelemetry traces/metrics for operational assurance.
Human oversight by design
- Human-in-the-loop checkpoints can be required by policy.
- Policy decision bundles provide a clear rationale trail.
- Approval-ready evidence packs for compliance and procurement teams.
- Controls are designed to make autonomy approvable, not opaque.
Need procurement-ready documentation?
We can share a reference security packet, evidence samples, and a pilot governance plan.