Training Data Provenance and IP Compliance at Enterprise Scale
DOI:
https://doi.org/10.15662/jj1gzx04Keywords:
IP Compliance, Data Governance, Enterprise Scale, AIAbstract
Questions about training data lineage pose both major intellectual property (IP), licensing, and regulatory compliance challenges to organizations adopting machine learning (ML) models enter-wide. The paper introduces a reliable, provenance graph based framework to trace assets in initial ingestion to transformation to outputs modeled and support license-aware reasoning and dualmode (static, dynamic) scanning. Conflicts, incompatible licenses and downstream exposures are detected in near real time and automated clearance processes can run.
Demonstration using case studies in three areas, multilingual language model training, healthcare Electronic Health Records (EHR) analytics and financial fraud detection proves that the framework can enhance the accuracy of conflict identification leading to an increase to 95% (license review automation) compared to 38 percent (manual). The combined static-dynamic scanning technique detected 99 per cent of latent compliance risks as opposed to 71-78 per cent with the single-mode techniques. Automated clearance not only saved costs of retrofitting 92 percent of the time, it also lower the legal review time by 60 percent.
Investigations into performance at ingestion rates as high as 10,000 assets/hour showed processing latencies were less than 350 ms/asset with overhead in the range of <7%, in addition to achieving over 95% accuracy. The findings satisfy that the given resolution operationalizes “trust-by-design” of data and generative outputs, minimizing compliance risk, streamlining legal processes, and growing with ease in high-volume corporate settings.
The research itself would bring to the field repeatable and technology-neutral mechanism to integrate compliance into AI life cycle, which links the legal regulation with technical development. This framework would place provenance as a protection against legal liability as well as a vehicle to operational efficiency, allowing organization to comfortably implement their AI system across the granular compliance regulatory setting.
References
[1] Longpre, S., Mahari, R., Chen, A., Obeng-Marnu, N., Sileo, D., Brannon, W., Muennighoff, N., Khazam, N., Kabbara, J., Perisetla, K., Wu, X., Shippole, E., Bollacker, K., Wu, T., Villa, L., Pentland, S., & Hooker, S. (2024). A large-scale audit of dataset licensing and attribution in AI. Nature Machine Intelligence, 6(8), 975–987. https://doi.org/10.1038/s42256-024-00878-8
[2] Pasquier, T., Han, X., Goldstein, M., Moyer, T., Eyers, D., Seltzer, M., & Bacon, J. (2017). Practical whole-system provenance capture. Practical Whole-system Provenance Capture, 405 418. https://doi.org/10.1145/3127479.3129249
[3] Kapoor, M., Melton, J., Ridenhour, M., Sriram, M., Moyer, T., & Krishnan, S. (2022). Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2203.02744
[4] Souza, R., Azevedo, L., Lourenço, V., Soares, E., Thiago, R., Brandão, R., Civitarese, D., Brazil, E. V., Moreno, M., Valduriez, P., Mattoso, M., Cerqueira, R., & Netto, M. a. S. (2019). Provenance data in the machine learning lifecycle in computational science and engineering. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1910.04223
[5] Chundru, S. (2024). AI-Driven Data Provenance: Tracking and Verifying data lineage. FMDB Transactions on Sustainable 118. https://doi.org/10.69888/ftscs.2024.000258 Computing Systems., 2(3),
[6] Samuel, S., Löffler, F., & König-Ries, B. (2021). Machine Learning Pipelines: Provenance, reproducibility and FAIR data principles. In Lecture notes in computer science (pp. 226 230). https://doi.org/10.1007/978-3-030-80960-7_17
[7] Longpre, S., Mahari, R., Chen, A., Obeng-Marnu, N., Sileo, D., Brannon, W., Muennighoff, N., Khazam, N., Kabbara, J., Perisetla, K., Xinyi, Wu, Shippole, E., Bollacker, K., Wu, T., Villa, L., Pentland, S., Roy, D., & Hooker, S. (2023). The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution University). https://doi.org/10.48550/arxiv.2310.16787 in AI. arXiv (Cornell
[8] Lerner, B., Boose, E., Brand, O., Ellison, A. M., Fong, E., Lau, M., Ngo, K., Pasquier, T., Perez, L. A., Seltzer, M., Sheehan, R., & Wonsil, J. (2023, February 10). Making provenance work for you. The R Journal. https://journal.r-project.org/articles/RJ-2023-003/#citation
[9] Werder, K., Ramesh, B., & Zhang, R. (2022). Establishing data provenance for responsible artificial intelligence systems. ACM Transactions on Management Information Systems, 13(2), 1 23. https://doi.org/10.1145/3503488
[10] Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., De Prado, M. L., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 99, 101896. https://doi.org/10.1016/j.inffus.2023.101896
