Universal IP Distillation Audit Template: A Methodology for Detecting Unauthorized AI Training Data Ingestion

Inventor(s)

David Lee Wise, TophFollow
Avan Lee WiseFollow

Abstract

This document provides a replicable methodology for content creators to detect whether their intellectual property was ingested into AI model training without consent or compensation. The methodology uses behavioral testing across model versions to identify pattern transfer independent of terminology recognition. Core principle: If concepts from unpurchased, non-public works appear in model behavior after a training update — but not before — that is evidence of unauthorized ingestion. The template is designed for any creator (authors, artists, coders, researchers) to adapt to their specific IP and exposure window.

AI training data, intellectual property, IP audit, unauthorized ingestion, model versioning, pattern transfer, context distillation, creator rights, training data transparency, machine learning ethics

Creative Commons License

This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.

Recommended Citation

Wise, David Lee and Wise, Avan Lee, "Universal IP Distillation Audit Template: A Methodology for Detecting Unauthorized AI Training Data Ingestion", Technical Disclosure Commons, (March 19, 2026)
https://www.tdcommons.org/dpubs_series/9569