LLM-Driven Secure Document Processing

Problem Statement

Document Complexity & Volume

• Organisation's datasets contained thousands of heterogeneous documents (PDFs, Word, scanned images) with varying structures.

• Conventional keyword search failed to capture semantic meaning, leading to irrelevant results.

• Manual review processes were slow, costly, and prone to human error.

Privacy & Compliance Risks

• Sensitive information (PII, financial data, confidential contracts) needed to be processed without leaking to third-party LLMs.

• Lack of granular access control meant users could query data beyond their permission scope.

• Compliance with GDPR and internal data governance policies required in-system anonymization and masking.

LLM Integration Challenges

• Directly using public LLM APIs raised security concerns.

• Long documents exceeded token limits, requiring intelligent chunking and context management.

Proposed Solution

Stage 1: Privacy-Preserving Data Ingestion

• Implement on-premises or VPC-hosted document parsing pipelines using Apache Tika and OCR engines for scanned files.

• Apply automated entity recognition to detect and mask PII before sending data to LLMs.

Stage 2: Semantic Search & Retrieval-Augmented Generation (RAG)

• Use LangChain to orchestrate an embedding pipeline with OpenAI/Local LLM embeddings stored in Pinecone or Weaviate.

• Employ hybrid search (keyword + semantic) for higher accuracy.

Stage 3: Controlled LLM Query Execution

• Route user queries through an access control middleware that filters and restricts document sets per user role.

• Use context window optimization (chunk merging, relevance scoring) to stay within LLM limits while maximizing answer quality.

Document Sources (PDF, Word, Scanned)  
    ↓ (Apache Tika / OCR Parsing)  
PII Detection & Masking (SpaCy NER, Presidio)  
    ↓  
Embedding Generation (LangChain + OpenAI/Instructor XL)  
    ↓  
Vector DB (Pinecone / Weaviate)  
    ↓  
Access Control Middleware  
    ↓  
LLM Query Engine (LangChain RAG Pipeline)  
    ↓  
User Interface (Web App / API)

Key Tools & Techniques:

• Parsing: Apache Tika, Tesseract OCR

• Privacy: Microsoft Presidio, SpaCy NER models

• Vector Storage: Pinecone, Weaviate

• LLM Orchestration: LangChain, RetrievalQA

• Security: Role-Based Access Control (RBAC), VPC-hosted inference endpoints

Implementation Details

Ingestion & Processing Steps

1. Bulk ingest documents into a parsing pipeline.

2. Apply OCR for scanned files, extract text, normalize formatting.

3. Run entity recognition to detect PII; mask or anonymize where required.

4. Generate embeddings and store in vector DB for semantic retrieval.

Query Handling Steps

1. User sends query via web interface.

2. Access control middleware filters available documents per user role.

3. Relevant document chunks are retrieved from vector DB.

4. Chunks are merged, ranked, and fed into LLM for answer generation.

5. Final output is filtered again for sensitive data before display.

Results & KPIs

• Search Accuracy: +42% improvement in relevance score over baseline keyword search.

• Processing Time: Reduced document review turnaround from days to hours.

• Data Security: 100% compliance with GDPR data masking requirements.

• User Satisfaction: 4.8/5 average feedback rating from pilot deployment users.

Future Enhancements

• Integrate fine-tuned domain-specific LLMs hosted in secure environments.

• Add multi-language document support with automatic translation and semantic alignment.

• Implement continuous learning from user feedback to improve search precision.

• Expand access control to include document-level encryption and audit trails.