AI Assurance Control Plane
An assurance layer over AI telemetry, evaluation, and review workflows, focused on evidence management rather than generic observability.
Problem / Scope
The control plane is positioned as an assurance layer above tracing, evals, and scanners. It is not meant to be a generic observability dashboard. The focus is evidence, review, retention, and audit workflows that connect findings to decisions.
Architecture
- Rules map incoming telemetry to findings
- Findings attach to evidence and review cases
- Incidents, retention decisions, and audit exports preserve downstream accountability
- Seeded demo wedge shows the workflows without depending on live enterprise integrations
Key Workflows / What It Proves
- Rules to findings to evidence to review decision
- Incident handling and retention or legal hold controls
- Audit packet generation from published artifacts
Limitations
- The seeded mode proves workflow shape, not full enterprise integration depth
- Public evidence may omit private customer-style datasets by design
- Claims should be bounded to the published screenshots, exports, and demo states
Evidence Pack
E-ASSURE-001
Dashboard and findings queue
Published screenshots showing the seeded oversight console and evidence drawer.
E-ASSURE-002
Review case decision
Timeline-based review state with explicit decision and rationale.
E-ASSURE-003
Retention and legal hold
Screenshot of the retention workflow and legal hold decision path.
E-ASSURE-004
Audit packet export
Stable snapshot of a generated audit packet or demo packet.
E-ASSURE-005
Source reference
Pinned repo commit or build tag for the published seeded demo state.