Cloud Data Classification
Methodology for cloud data classification covering discovery, sensitivity categorization, labeling, and integration with protection policies using Macie, Purview, and DLP.
Methodology for cloud data classification covering discovery, sensitivity categorization, labeling, and integration with protection policies using Macie, Purview, and DLP.
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Cloud data classification is the methodology of discovering, categorizing, and labeling data stored across cloud services based on sensitivity, regulatory requirements, and business value. It provides the foundation for data protection by ensuring organizations know what data they have, where it resides, and how it should be protected.
Data classification operates in three phases. Discovery scans cloud storage (S3, Azure Blob, GCS), databases, and SaaS applications to create a comprehensive data inventory. Classification applies rules and machine learning to categorize data into sensitivity levels: public, internal, confidential, and restricted. Methods include pattern matching for structured data like credit card numbers and social security numbers, NLP-based classification for unstructured content, and metadata analysis for file properties. Labeling applies tags or metadata to classified data enabling downstream policy enforcement. AWS Macie discovers and classifies sensitive data in S3 using ML. Azure Purview (now Microsoft Purview) classifies data across Azure, AWS, and on-premises sources. Google DLP (Cloud Data Loss Prevention) identifies sensitive data across Google Cloud services. Sensitivity labels integrate with access controls, encryption policies, and DLP rules to enforce classification-based protection. Data catalogs maintain the classification inventory with lineage tracking showing how data flows between systems.
Organizations cannot protect data they do not know about. Cloud storage makes it trivially easy to create new data stores that accumulate sensitive information outside security team visibility. Data classification transforms the abstract goal of data protection into concrete, enforceable policies based on actual data sensitivity and location.
CDA addresses data classification under the DPS (Data Protection and Sovereignty) domain. Our missions deploy classification tooling across cloud estates, establish sensitivity taxonomies aligned with regulatory requirements, and integrate classification labels into access control and encryption policies.
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Written by CDA Editorial
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