IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v12y2026i2id1975.html

SecureRAG: Offline Document Analysis using Local Large Language Models

Author

Listed:
  • Gayatri Gawade
  • Medha Wadekar
  • Manish Shinde
  • Prachi Desai
  • Bhushan Bhokse

Abstract

Critical weaknesses in enterprise data governance have been intensified by the widespread adoption of cloud-based AI services, particularly for organizations operating under strict regulatory frameworks such as HIPAA, GDPR, SOC 2, and ISO 27001. Although Retrieval-Augmented Generation (RAG) systems have proven highly effective for document intelligence tasks, their conventional architectures require sensitive data to be relayed to external third-party inference endpoints — a practice incompatible with industries such as financial services, healthcare, legal, and defense. This paper introduces SecureRAG, a fully offline, enterprise-grade document intelligence system engineered for air-gapped and network-restricted environments. The system empowers organizations to locally upload, index, and semantically query confidential PDF corpora using on-device Large Language Models (LLMs), ensuring zero data transmission to external infrastructure.

Suggested Citation

  • Gayatri Gawade & Medha Wadekar & Manish Shinde & Prachi Desai & Bhushan Bhokse, 2026. "SecureRAG: Offline Document Analysis using Local Large Language Models," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 12(2), pages 735-738, April.
    • Handle: RePEc:jbh:ijsrcs:v12:y2026:i2:id:1975
    DOI: 10.32628/CSEIT261213105
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT261213105
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT261213105
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT261213105/CSEIT261213105
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT261213105?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v12:y2026:i2:id:1975. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.