# LLM-Powered Data Classification at Grab ## Overview Grab, a leading Southeast Asian super-app platform, implemented a sophisticated LLM-powered data classification system to handle their massive PetaByte-scale data infrastructure. The system automates the classification of sensitive data and generation of governance-related metadata, replacing what was previously a manual and time-consuming process. ## Technical Implementation ### System Architecture - Orchestration Service (Gemini) ### LLM Integration Considerations - Context Length Management - Resource Management ### Prompt Engineering Strategy - Clear Task Definition - Best Practices Implementation ### Data Processing Pipeline - Input Processing - Output Handling ### Monitoring and Evaluation - Accuracy Metrics - Performance Tracking - Cost Efficiency ## Production Deployment ### Integration Points - Metadata Management Platform - Production Database Management Platform - Data Governance Systems - Attribute-based Access Control (ABAC) Systems ### Scalability Considerations - Request Batching - Rate Limiting ### Verification Workflow - User Review Process ## Challenges and Solutions ### Initial Challenges - Manual Classification Limitations - Third-party Tool Issues ### LLM-based Solutions - Natural Language Interface - Automated Classification ## Future Developments ### Planned Improvements - Enhanced Prompt Engineering - Evaluation Framework ### Scaling Plans - Platform Integration - Use Case Expansion ## Impact and Results ### Quantitative Benefits - Time Savings - Processing Efficiency ### Qualitative Improvements - Enhanced Data Governance - Operational Efficiency ### Business Value - Cost-effective scaling solution - Improved data security and governance - Enhanced operational efficiency - Foundation for advanced data management features # LLM Implementation for Enterprise Data Classification at Grab ## Background and Problem Statement Grab, a leading Southeast Asian super-app platform, faced significant challenges in managing and classifying their PetaByte-scale data infrastructure. The company handles countless data entities including database tables and Kafka message schemas, requiring careful classification of sensitive information for proper data governance. - Initial challenges: ## Technical Solution Architecture ### Orchestration Service (Gemini) The system implements a sophisticated orchestration service with several key components: - Core Components: - Technical Constraints Management: ### LLM Implementation Details The system employs several sophisticated prompt engineering techniques: - Prompt Design Principles: - Classification Process: ### Integration and Data Flow The system implements a comprehensive data flow: - Data Processing Pipeline: ## Operational Aspects ### Performance Metrics The system has demonstrated impressive operational metrics: - Processing Capacity: - Efficiency Gains: ### Quality Control and Monitoring The implementation includes several quality control mechanisms: - Verification Workflow: ### Infrastructure and Scaling The system is designed for enterprise-scale operations: - Technical Infrastructure: ## Future Developments The team has outlined several areas for future enhancement: - Prompt Engineering Improvements: - Performance Monitoring: - Scale-Out Plans: ## Best Practices and Learnings Key insights from the implementation: - Prompt Engineering: - System Design: - Integration Considerations: The implementation serves as a model for enterprise-scale LLM operations, demonstrating how careful system design, prompt engineering, and operational considerations can create an effective, production-grade LLM-based classification system.