AutoClassifier – AI/ML-Powered Data Classification & Tagging Accelerator

Overview: AutoClassifier is an AI/ML-powered data classification and tagging accelerator designed to automate end-to-end data confidentiality classification across enterprise data estates. Originally designed and implemented for a large CPG client, AutoClassifier was created to address the significant challenges of manual and inconsistent data confidentiality tagging across large, distributed data environments where high operational effort, compliance overhead, and error-prone spreadsheet-based tagging led to delayed issue identification and increased risk. The accelerator is now part of Coforge Data CosmosTM which is our Innovation Backbone combining platforms, agentic accelerators, and services to enable end-to-end data engineering, BI, governance, and analytics. AutoClassifier can be readily reproduced and adapted for clients facing similar data governance, compliance, and classification requirements.

Why AutoClassifier:

1. Classification Effort Reduction Eliminates tedious, error-prone manual spreadsheet-based tagging. Traditional classification requires data stewards to manually review and tag thousands of data elements — a process that is slow, inconsistent, and does not scale. AutoClassifier automates this with rule-based logic and AI/ML models, reducing manual classification work by over 60%.

2. Scale & Consistency Ensures uniform tagging across enterprise-scale datasets spanning multiple databases, data lakes, and cloud warehouses. Whether classifying 100 or 100,000 data elements, AutoClassifier applies consistent classification logic with 75–90% re-run stability — significantly outperforming manual approaches and general-purpose AI tools (<60% stability).

3. Compliance & Auditability Meets regulatory requirements through transparent, traceable classification workflows. Every classification decision is logged with confidence scores, rule references, and human validation records — providing audit-ready evidence for GDPR, HIPAA, PCI DSS, and internal governance.

4. Time & Cost Optimization Minimizes operational expenses and project timelines by automating the most labor-intensive phase of data governance programs. Accelerates data onboarding and frees data stewards to focus on governance strategy rather than manual tagging.

How It Works:

1. Preparation — Ingest source files and metadata for preprocessing. Connect to databases, data lakes, and catalog systems to extract schema metadata, column names, sample data, and existing classifications.

2. Classification & Tagging — Apply rule-based logic (regex patterns, keyword matching, data type analysis) and AI/ML models trained on domain-specific patterns to classify and tag data elements with confidentiality levels, sensitivity categories, and data domains.

3. Human-in-the-Loop (HITL) — Manual checkpoints for verification and quality scoring. Data stewards review AI-generated classifications, approve or override tags, and provide feedback for continuous learning. Ensures governance accountability while maintaining automation speed.

4. Continuous Learning — Feedback-driven retraining to improve model precision. Every human correction is captured as training signal, enabling models to adapt to organization-specific patterns.

5. Integration & Outputs — Export classified metadata to data catalogs (AWS Glue Data Catalog), business glossaries, and enriched metadata repositories for downstream governance workflows.

Key Benefits: • Automation — Reduces manual classification work by 60%+ • Accuracy & Consistency — 75–90% re-run stability via AI/ML • Faster Turnaround — Accelerates data onboarding and project delivery • Reduced Manual Effort — Streamlines workflows for data stewards • Cost-Effective — Lowers operational costs through scalable classification • Audit-Ready — Complete traceability for compliance

Industry Applications: • CPG & Retail — Automated confidentiality classification across product, supply chain, and customer data for GDPR and regional privacy compliance. • Banking & Financial Services — Sensitivity classification across customer, transaction, and risk data for BCBS 239, PCI DSS. Supports dynamic access control based on classification tags. • Insurance — Policyholder PII classification across policy admin, claims, and billing. Enables Solvency II governance evidence. • Healthcare — PHI detection across EMR/EHR systems for HIPAA minimum necessary access enforcement. • Travel & Hospitality — Guest and passenger data classification for GDPR consent management and PCI DSS compliance.

Cloud-Native Deployment on AWS: Deployed on Amazon EKS. Amazon Bedrock provides AI/ML reasoning. Amazon S3 stores source data and outputs. AWS Glue Data Catalog for metadata management. Amazon SageMaker for model training and retraining workflows.