Privacy, Retention, And Right To Delete
Overview
Use this skill when the data platform must respect privacy obligations as an engineering behavior, not a policy slide. It helps agents make retention, deletion, masking, and minimization explicit across storage, transformations, and publishes.
When to Use
- handling personal or regulated data
- defining retention rules
- implementing deletion or erasure requests
- changing how sensitive fields are stored, copied, or published
- validating that downstream systems do not retain data longer than allowed
Do not assume masking alone satisfies retention or deletion obligations.
Workflow
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Classify the data and obligations. Clarify:
- sensitive fields
- retention limit
- deletion trigger
- legal hold exceptions
- downstream replication paths
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Map where the data lives. Include:
- raw landing
- transformed tables
- serving layers
- extracts
- caches and feature stores
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Define the enforcement path. Decide how the system will:
- prevent unnecessary copies
- enforce retention windows
- process deletions
- prove compliance actions happened
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Validate downstream propagation. Deletion in one layer is not enough if copies remain elsewhere.
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Record exceptions and audit evidence.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "The source system already deleted it." | Downstream data products may still retain copies. |
| "We only use hashed identifiers." | Hashing does not eliminate all privacy or retention duties. |
| "We can clean up old data later." | Retention failures often become expensive compliance incidents. |
Red Flags
- no retention schedule exists
- deletion requests stop at one system boundary
- old extracts and caches are ignored
- privacy controls rely on undocumented manual steps
Verification
- Sensitive data and retention obligations are classified
- All material storage locations and copies are mapped
- Deletion and retention enforcement are explicit and testable
- Audit evidence or runbooks exist for compliance actions
