Overview#
Assigning sensitivity labels to data based on business impact. Guides encryption, access control, and retention policies across environments.
Core objectives#
- Establish shared definitions of Data Classification for security, engineering, and leadership teams.
- Connect Data Classification activities to measurable risk reduction and resilience goals.
- Provide onboarding notes so new team members can quickly understand how Data Classification works here.
Implementation notes#
- Identify the primary owner for Data Classification, the data sources involved, and the systems affected.
- Document the minimum viable process, tooling, and runbooks that keep Data Classification healthy.
- Map Data Classification practices to standards such as ISO/IEC 27001, NIST CSF, or CIS Controls.
Operational signals#
- Leading indicators: early warnings that Data Classification might degrade (e.g., backlog growth, noisy alerts, or missed SLAs).
- Lagging indicators: realized impact that shows Data Classification failed or needs investment (e.g., incidents, audit findings).
- Feedback loops: retrospectives and metrics reviews that tune Data Classification continuously.
Related practices#
- Align Data Classification with defense-in-depth planning, threat modeling, and disaster recovery tests.
- Communicate updates to stakeholders through concise briefs, dashboards, and internal FAQs.
- Pair Data Classification improvements with tabletop exercises to validate expectations.