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Abstract

Summary

The adoption of Generative Artificial Intelligence (AI) within highly regulated industries poses unique technical and governance challenges, particularly when strict data sovereignty requirements prohibit the use of cloud-based AI services. In the Indonesian upstream oil and gas sector, SKK Migas operates under a mandate of zero data exfiltration, requiring all analytical and AI workloads to be executed entirely within on-premise infrastructure.

This paper presents the design, implementation, and empirical evaluation of an on-premise Generative AI system developed to support two critical operational needs: (1) the interpretation of complex regulatory documentation and (2) analytical querying of large-scale structured reserve databases. The study was conducted between July and December 2025 and focuses on practical architectural decisions made under severe computational constraints.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202639124
2026-03-09
2026-02-07

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References

  1. A. N.Hadie, I.Tahyudin, and T.Hariguna, “Enhancing Accessibility in Local Government Data Portals via Retrieval- Augmented Generation: A Case Study on Satu Data Indonesia in Banyumas Regency”, J. Tek. Inform. (JUTIF), vol. 6, no. 4, pp. 2420–2433, Sep. 2025.
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/content/papers/10.3997/2214-4609.202639124
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On-Premise Generative AI for Indonesia’s Sovereign Oil & Gas Reserve Data
Conference Proceedings 2026, 1 (2026); https://doi.org/10.3997/2214-4609.202639124
/content/papers/10.3997/2214-4609.202639124
/content/papers/10.3997/2214-4609.202639124
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