Lineage, Traceability, and Reproducibility as Reliability Requirements in Enterprise AI Systems

Authors

  • Divya Bonthala

Keywords:

Enterprise Artificial Intelligence, Traceability, Data Lineage, Reproducibility, Data Governance, AI Reliability, Model Versioning

Abstract

Artificial intelligence is being applied to key business and compliance choices by more systems in the enterprise. One of the most common systems is concerned with the accuracy of the model and does not factor in the reliability aspect, like the lineage or traceability, or reproducibility. In this paper, we obtain these three aspects as fundamental reliability expectations of enterprise AI. The study was a real enterprise AI applied in 12 months with a before and after quantitative design. Lineage coverage, version control and reproducibility controls were introduced thus, the lineage coverage rose to 0.91 and the success of reproducibility rose to 92% after these tools were applied on structured lineage. Rapid time to incident investigation was less by 66%, audit preparation was also less by 62% and compliance findings were also less by 75%. Monte Carlo simulation also indicated that the risk variability was smaller when the lineage controls had been incorporated. This observation is in full agreement with the results that indicated that integrating lineage, traceability, and reproducibility into AI platforms enhances reliability, audit readiness, and trust in AI results.

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Published

25.03.2026

How to Cite

Divya Bonthala. (2026). Lineage, Traceability, and Reproducibility as Reliability Requirements in Enterprise AI Systems. International Journal of Intelligent Systems and Applications in Engineering, 14(1s), 261–269. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8170

Issue

Vol. 14 No. 1s (2026)

Section

Research Article

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