PROJECT OVERVIEW
Abstract
This case study presents the design and development of a secure conversational search engine tailored for the Indian defence sector. This system utilizes large language models (LLMs) to provide precise, contextually relevant answers based solely on information from a restricted, secure database. Key features include citation-based responses, advanced access controls for information hierarchy, and a specialized ranking mechanism for managing sensitive data visibility according to the official rank. By ensuring data security, ease of access, and user-centric design, this tool addresses critical challenges in managing classified and semi-classified military information.
Problem Identified
MilitaMilitary operations require precise and timely access to data. However, the inherent risks of exposing sensitive information necessitate a platform that balances usability with stringent security requirements. Specific challenges include:
Lack of Secure Conversational Interfaces: Existing AI systems like Perplexity are unfit for defence applications due to their reliance on open web data.
Data Access Hierarchy: No effective mechanism exists for hierarchical control of data access based on ranks or roles within the defence ecosystem.
Citation Accuracy: Defence personnel require trustworthy responses citing exact locations in the secure database.
Developed Solution
The proposed solution follows a modular architecture comprising:
Data Repository Module:
A structured, indexed database containing classified documents categorized by confidentiality level.Data Ingestion Pipeline: Automates document ingestion with metadata tagging for sensitivity and keywords.
Encryption Standards: Ensures compliance with national defence encryption protocols.
Conversational Search Engine Core:
Powered by fine-tuned LLMs designed to perform extractive and abstractive reasoning over the secure dataset.LLM Fine-Tuning: Customized on a dataset with classified language patterns to improve accuracy and understanding of military terminology.
Access Management Layer:
Role-based access control (RBAC) integrated into the system, leveraging organizational hierarchy.Citing Mechanism:
Dynamic generation of inline citations with hyperlinks to corresponding data sections.
Results and Evaluation
Prototype Deployment
The initial prototype was deployed in a simulated environment with realistic datasets from declassified military archives. Performance was measured across three key metrics:
Response Accuracy: 94% accurate retrieval with proper contextual understanding.
Access Compliance: 100% adherence to rank-based visibility.
User Satisfaction: Average user satisfaction score of 9.2/10 across 50 defence personnel.
Key Observations
Enhanced operational efficiency: Data retrieval times reduced by 72% compared to traditional methods.
Minimal training required due to the intuitive interface.
User concerns included occasional over-conciseness in responses, which is addressed through iterative model updates.
