Data classification is the systematic process of organizing data into categories based on its sensitivity, value, and regulatory requirements. In the context of AI governance, data classification determines what information can be safely used with AI tools and what must be restricted.
Common classification levels include: Public (freely available, safe for any AI tool), Internal (business information, approved enterprise AI tools only), Confidential (sensitive data that could cause harm if exposed, restricted from external AI tools), and Restricted (highly sensitive data under legal/regulatory protection, prohibited from all AI tools).
For AI governance specifically, data classification is critical because: AI tools may retain and learn from input data, prompts and file uploads can expose classified information, AI-generated outputs inherit the classification of their inputs, and regulatory frameworks require data protection controls for AI processing.
Implementation requires clear policies mapping data types to classification levels, employee training on classification procedures, technical controls (DLP, access controls) enforcing classification rules, regular audits of classification accuracy, and automated classification tools for large data volumes.
