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