Purpose before collection
Collect or expose data only when the workflow has a clear purpose and the data is actually needed.
Privacy engineering
Design data handling as part of the system architecture
Privacy engineering starts before a privacy notice. It reduces unnecessary collection, clarifies access and retention, constrains logs and integrations, and gives teams a concrete map of where sensitive information can flow.
Data minimization
Reduce fields, precision, history and copied datasets so systems do not retain more information than the task requires.
Access boundaries
Restrict who and what can access sensitive data, including background jobs, support tooling, AI retrieval and logs.
Retention & deletion
Define how long data is needed, where copies exist and how deletion propagates through backups, indexes and integrations where applicable.
Logging discipline
Keep useful operational evidence without casually copying secrets, message bodies, identifiers or sensitive payloads into logs.
Third-party handling
Map which providers receive data, for what purpose, through which integration and what happens if the dependency changes.
User-facing clarity
Make important collection, sharing or local-storage behavior understandable at the point it matters.
Secure defaults
Prefer the least exposed, least permissive and least persistent default that still supports the real task.
This is engineering guidance, not legal advice. Applicable privacy and data-protection obligations depend on the real organization, jurisdiction, data and processing purpose.
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Need help mapping data through a system?
Start with the data categories, purpose, users, storage, integrations, logs, retention assumptions and access roles involved in the workflow.