TPN Intelligence Framework: Automated License and Compliance Risk Analysis from Third‑Party Notices

Third‑Party Notices (TPNs) serve as critical compliance artifacts that document the open‑source licenses and obligations associated with software components used in commercial products. Traditional compliance tools primarily rely on source‑code scanning or binary analysis to infer licensing information. Still, these approaches often fail to address scenarios where vendors supply only notice documents or when source access is limited. This creates significant inefficiencies and introduces compliance risk, especially in large‑scale software supply chains.

This work presents Automated License Intelligence, a machine‑learning–based framework designed to analyze, classify, and extract licensing signals directly from TPN documents. The system processes unstructured notice text, applies natural language processing (NLP) techniques to detect license types, and evaluates potential compliance risks based on license attributes, reuse conditions, attribution requirements, and cross‑document inconsistencies. By treating TPNs as first‑class compliance artifacts, the framework enables a new class of automation workflows that extend beyond conventional code‑centric methods.

The proposed approach supports:

• Automated extraction of key license metadata from free‑form notice text.
• ML‑driven classification of software licenses, obligations, and risk categories.
• Cross‑document consistency analysis to identify mismatches and disclosure issues.
• Integration with compliance governance workflows for enterprise‑scale operations.

Results demonstrate that machine‑learning–based analysis can meaningfully enhance accuracy, reduce manual review time, and improve the detection of compliance risks present within third‑party notices. The research contributes an extensible foundation for advancing automation in open‑source compliance, software governance, and documentation intelligence.