Data discovery and classification helps locate and define personal data
Data discovery and classification are foundational to data privacy, governance, and protection. Enterprise data discovery scans data storage systems such as cloud, Big Data, relational, file, and mainframe to determine where personal and sensitive data is located.
Classification evaluates the data and classifies it by type; for instance, health data, personal data, financial data, etc. DPIAs, DSARs and the implementation of safeguards and controls for personal information need this intelligence to accurately manage tasks and risk.
How is data discovery and classification accomplished?
Tools are available that provide discovery and classification to some degree. But these tools are for specific domain applications and do not scale or provide capabilities for identity and risk. New purpose-built tools for enterprise privacy and security have emerged over the last few years. For example, ML-driven data discovery and classification to discover data across all platforms and data types. In summary, discovery and classification is accomplished by:
- Manual efforts via surveys and interviews which feed:
- Data architecture diagrams and solutions.
- Data catalogs.
- Domain-specific discovery solutions, such as file analysis or privacy tools that provide simple search tools.
- Automated and ML-driven data discovery and classification solutions such as Integris that provide enterprise-scale discovery and classification.