Institutional security for unreleased models and proprietary datasets.
AI evaluation frequently involves confidential model weights, unreleased features, proprietary codebase context, and sensitive prompts. We treat client data with strict compartmentalization and air-gapped review practices.
The 6 Pillars of Confidential AI Evaluation
Isolated Customer Tenancy
Dedicated logical and physical segregation of evaluation data. Zero cross-contamination between client evaluation queues.
Air-Gapped Reviewer Sandboxes
Sandboxed browser environments with clipboard restrictions, watermark overlays, and zero local device persistence.
Automated & Manual PII Scrubbing
Pre-ingestion scrubbing of API keys, employee PII, proprietary source code tokens, and internal database URIs.
Role-Based Access Control (RBAC)
Least-privilege access model. Reviewers only see the exact tasks assigned to their authenticated session.
Strict NDA & Legal Perimeter
100% of internal evaluators are background-verified and bound by strict confidentiality and intellectual property agreements.
Immutable Audit Trails
Every score, reviewer edit, QA sign-off, and export is timestamped and recorded in auditable append-only logs.
Transparent Security & Data Governance
We believe in honest, demonstrable security practices rather than unverified badge claims. Our infrastructure is built for enterprise audits, SOC 2 alignment, and strict client segregation.