Data Classification and Protection
Overview
The AWS Partner has methodology, process and relevant tooling experience to enable customers in identification, analysis, classification and cataloguing of data, determining applicable compliance frameworks, and advising on appropriate auditability of the data.
Evidence Documentation
1. Service Offering on Data Classification and Protection Capabilities
Data Identification and Analysis Services
Our methodology enables comprehensive data discovery across customer AWS environments using Amazon Macie for sensitive data identification and AWS Config for configuration analysis. Data discovery includes structured database analysis, unstructured S3 scanning, and real-time stream analysis to ensure complete coverage of customer data assets.
Data profiling services analyze quality, completeness, and sensitivity patterns through statistical analysis and pattern recognition for personally identifiable information (PII). Content analysis utilizes natural language processing to identify financial data, healthcare information, and intellectual property across various formats.
Data Classification and Cataloguing Services
Our classification framework implements a four-tier sensitivity model: Public, Internal, Confidential, and Restricted, with corresponding protection requirements. Classification services include automated labeling through AWS Macie, manual workflows for edge cases, and continuous validation through periodic rescanning.
Data cataloguing creates comprehensive inventory using AWS Glue Data Catalog for structured data and custom repositories for unstructured data. Catalog management includes asset registration, metadata enrichment, and lifecycle tracking to support compliance reporting and access control.
Compliance Framework Determination
Compliance assessment services evaluate customer data against regulatory requirements including GDPR, HIPAA, SOX, PCI-DSS, and industry-specific regulations. Assessment includes gap analysis, risk evaluation, and compliance roadmap development through automated policy engines and expert consultation.
Industry-specific compliance provides tailored guidance for healthcare, financial services, retail, and government sectors. Multi-jurisdiction compliance addresses overlapping requirements and conflicting obligations through jurisdiction mapping and requirement harmonization.
Data Auditability Services
Audit trail services establish comprehensive logging for data access, modification, and movement using AWS CloudTrail for API logging and AWS Config for configuration changes. Compliance reporting generates required documentation for regulatory audits through automated dashboards and periodic reports.
Continuous monitoring provides ongoing compliance validation through automated testing, policy enforcement, and exception reporting with real-time compliance checking and remediation workflow automation.
2. Data Anonymization and Tokenization Mechanisms
Anonymization Techniques
Statistical anonymization implements k-anonymity, l-diversity, and t-closeness algorithms to ensure individual privacy while maintaining data utility. Implementation utilizes AWS Glue for data transformation and custom algorithms for specific use cases.
Data masking services implement format-preserving encryption, deterministic masking, and reversible pseudonymization. Differential privacy adds controlled noise to statistical queries to prevent individual identification while maintaining statistical accuracy.
Tokenization Systems
Format-preserving tokenization replaces sensitive data elements with tokens that maintain original format characteristics. Implementation includes secure token vaults, generation algorithms, and detokenization services to ensure data usability while providing strong protection.
Vaultless tokenization eliminates token storage requirements through cryptographic transformation using AWS KMS for key management and custom encryption algorithms. Database-level tokenization implements transparent tokenization within database systems through triggers and stored procedures.
Advanced Protection Mechanisms
Homomorphic encryption enables computation on encrypted data without decryption for privacy-preserving analytics. Secure multi-party computation enables collaborative analysis across multiple parties without revealing individual data. Zero-knowledge proof systems enable verification of data properties without revealing actual content.
Implementation Approach
Implementation begins with data discovery and classification framework development, followed by protection mechanism deployment including tokenization systems and anonymization pipelines. Service delivery includes methodology documentation, tool deployment, and staff training.
Success Metrics
Effectiveness measures include classification accuracy above 95%, tokenization performance within 10ms latency, and anonymization utility preservation above 85%. Compliance metrics include audit trail completeness at 100% and zero privacy incidents.
This document provides evidence of our data classification and protection capabilities including automated discovery, multi-tier classification, compliance framework determination, and anonymization and tokenization mechanisms.