Purpose of the Standard

The Standard provides a unified, enforceable framework for dataset integrity, provenance verification, and vendor‑independent AI development. It protects institutions from hidden liabilities, unstable vendors, and unverifiable training sources by defining the minimum conditions required for trustworthy intelligence. As a recognized stakeholder in the global AI standards ecosystem, ATIC translates high‑level frameworks into concrete, certifiable requirements organizations can operationalize.

Scope of the Standard

The Provenance & Integrity Standard applies to:

• datasets and training corpora

• synthetic data pipelines

• model development workflows

• vendor‑provided AI systems

• internal enterprise AI systems

• public‑facing and internal deployments

It governs both System Certification (for builders) and Organization Certification (for institutions deploying AI).

Core Principles

• Lawful Origins

  All data must be obtained, licensed, or created through legitimate means.

• Traceable Lineage

  Every dataset must have a verifiable chain of custody.

• Structural Integrity

  Data must be free from contamination, duplication, and synthetic instability.

• Vendor Independence

  Institutions must maintain sovereignty over their data and models.

• Defensible Intelligence

  AI systems must be auditable, explainable, and legally resilient.