Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Bhupinder; Shastri, Arunima; Chandra, Saurabh Title A name given to the resource Assessing Security and Privacy Concerns in LLM Applications: Legal and Social Concerns Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Theory, Practice, and Future Direction of Large Language Models;pp.227-249 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-8387-2.ch008" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-8387-2.ch008</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021208470?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021208470?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh B., Sharda University, India; Shastri A., Christ University, Bangalore, India; Chandra S., Bennett University, Greater Noida, India Description An account of the resource Large language models (LLMs) like Open AI's GPT- 4 have transformed many industries, including natural language processing. But, still the applications of these need a properly secure mechanism as it is getting attached to everyday technology at on an extensive rate and large security &amp; privacy risk are encapsulated in its use which must be controlled for safe and ethical use. LLM models are trained with an avalanche of data, often from web- scraped sources that could be handling personal and sensitive content. This may inadvertently have the LAZ model hold on to, and possibly leak, some private data of users who use the API. Having measures to address them through comprehensive strategies and conforming with regulations are important in the responsible usage of LLM applications. 2026 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836938389-6; 979-836938387-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online