AI Output Retention and Deletion
Define and enforce retention schedules and deletion procedures for AI-generated content, decisions, and the personal data contained within them.
Objective
Comply with data retention obligations and individuals' deletion rights by managing the lifecycle of AI outputs as carefully as the inputs.
Maturity Levels
Initial
AI outputs are retained indefinitely with no deletion procedures.
Developing
Retention periods exist informally but deletion is not systematically executed.
Defined
Documented retention schedules apply to AI outputs by category, with automated deletion workflows.
Managed
Deletion execution is audited; deletion requests from individuals are processed within defined SLAs.
Optimizing
Retention policies adapt automatically to regulatory changes; deletion capability is tested regularly.
Evidence Requirements
What an auditor or assessor would expect to see for this control.
- —Output retention policy specifying periods by output type and system risk tier, approved by Compliance and DPO
- —Automated deletion workflow configuration and execution records confirming outputs are deleted on schedule
- —Erasure request records showing AI-generated outputs containing personal data were located and deleted on request
- —Retention exception records for outputs subject to legal hold, with Legal sign-off
- —Annual retention review confirming policy remains appropriate against current regulatory and business requirements
Implementation Notes
Key steps
- Classify AI outputs: decision records (longer retention for accountability), conversational outputs (shorter, privacy-sensitive), and system logs (per ALC-003).
- Implement right-to-erasure workflows: if your AI system processes personal data, you need a documented process for responding to deletion requests that includes AI-generated outputs about that individual.
- Test deletion is complete: verify that deleting a record removes it from all storage locations including backups, caches, and downstream systems.
- For generative AI outputs, assess whether outputs derived from personal data are themselves personal data — they often are.
Example Implementation
B2C app using AI for personalized recommendations and customer support chat
AI Output Retention Schedule — Consumer App
| Output Type | Examples | Retention | Basis | Deletion Method |
|---|---|---|---|---|
| Recommendation results | Product recommendations shown to user | 90 days | Analytics need; GDPR minimization | Automated TTL |
| Support chat transcripts | AI + human chat history | 1 year | Quality assurance; complaint handling | Automated + erasure request handling |
| AI decision records (adverse) | Flagged-account AI decisions | 3 years | Accountability; potential disputes | Manual with Legal sign-off |
| Evaluation/test outputs | Internal model testing outputs | 6 months | Development use | Automated TTL |
Right-to-erasure process:
- User submits deletion request via account settings
- System identifies all AI outputs linked to user ID
- Outputs deleted from primary store within 7 days
- Deletion propagated to backup stores within 30 days
- Completion notification sent to user
- Deletion record retained for 12 months (proof of compliance, no personal data)
Control Details
- Control ID
- DGC-004
- Domain
- Data Governance
- Typical owner
- Privacy / Legal / AI Engineering
- Implementation effort
- Medium effort
- Agent-relevant
- Yes