Data Lifecycle & Cleanup
AiX deployments may accumulate multiple classes of operational and business data. Cleanup must be deliberate and must not remove records required for audit, recovery or customer retention requirements.
| Data Area | Examples | Cleanup Consideration |
|---|---|---|
| Database | application, execution, interaction records | retention and referential integrity |
| Audit | user/actions/AI interaction audit | compliance retention |
| File Storage | input, working, output, temporary files | business retention and active jobs |
| Knowledge Base | source files, chunks, vectors | KB lifecycle and re-index requirements |
| Logs | application/service logs | rotation and retention |
| Cache | temporary/cache data | safe expiry/invalidation |
| AI Worker | temporary processing artifacts | active-job safety |
Cleanup Principles
- Define retention before automated deletion.
- Separate temporary/cache cleanup from business-record deletion.
- Preserve required audit/provenance records.
- Validate backups where recovery may be required.
- Record customer-specific retention policies in the customer repository.