Intelligent Framework for Open Source Software License Verification and Compliance

Abstract

Modern digital ecosystems rely heavily on Open Source Software (OSS), but maintaining license compliance is still a major and unsolved problem. Current approaches rely on either manual audits, which are expensive, sluggish, and prone to error, or automatic scanners, which frequently fail with dual or bespoke licenses. Businesses, entrepreneurs, and academic institutions are exposed to serious legal, financial, and reputational concerns as a result of this divide. This project suggests a multi-layered OSS License Verification Framework that incorporates human-in-the-loop learning, logical reasoning, evidence-based validation, provenance tracking, and cryptographic assurance in order to overcome these constraints. To establish technical ground truth, the system starts with SBOM and SPDX provenance data, builds an attestation graph, and uses binary inference and differential tracing. License requirements are represented as vectors of obligations, assessed using a constraint solver and validated using zero-knowledge proofs (zk-proofs) to give auditors reliable proof of compliance. A human oracle ensures adaptation to changing license ecosystems by resolving ambiguities and continuously enhancing the knowledge base. The suggested framework seeks to provide an end-to-end, intelligent, and auditable solution for OSS licensing compliance by fusing automation with verifiability and adaptability. The results will help a variety of stakeholders, such as businesses looking to reduce risk, startups seeking quicker innovation, and academic institutions using OSS responsibly, all of which will contribute to a more secure and reliable opensource ecosystem.