What is an AI data retention policy?

It is a governance policy that defines how long AI prompts and outputs are stored, who can access them, and how deletion requests are handled.

Who should own AI retention policy reviews?

Most teams assign shared ownership to compliance, security, and AI operations, with one governance owner accountable for policy updates.

How often should retention controls be reviewed?

High-impact AI workflows are usually reviewed monthly, and immediately after incidents, vendor changes, or regulatory updates.

AI Data Retention Policy Generator

Create a production-ready retention policy for AI prompts and outputs with control ownership, deletion standards, and audit evidence checkpoints.

Generate an audit-ready retention policy for AI prompts and outputs with owner-assigned controls, deletion standards, and exportable policy artifacts.

Team nameWorkflow namePolicy scopeRegulated data exposureData sensitivityVendor training opt-outPrompt retention (days)Output retention (days)Deletion SLARedaction coverageSubprocessor countReview cadence

Retention risk score: 4/5 • High control urgency

Control rows: 7 total (3 P0, 4 P1)

Markdown output preview

# AI Data Retention Policy - Customer support assistant

## Policy context
- Team: AI Governance Office
- Scope: Customer-facing assistants
- Regulated data exposure: Potential
- Data sensitivity: High
- Prompt retention window: 30 days
- Output retention window: 30 days
- Vendor training opt-out status: Contractually enforced
- Deletion SLA: 8-30 days
- Redaction coverage: Partial masking
- Subprocessor count: 3
- Review cadence: Monthly

## Risk summary
- Retention risk score (1-5): 4
- Risk band: High control urgency
- P0 controls: 3
- P1 controls: 4

## Policy control table
| # | Domain | Policy rule | Owner | Evidence | Retention window | Priority |
|---|---|---|---|---|---|---|
| 1 | Data Classification | Classify AI prompts and outputs by sensitivity tier before storage or third-party transfer. | Data Protection Lead | Data classification matrix + workflow mapping | Review monthly | P0 |
| 2 | Prompt and Output Storage | Store only minimum required prompt/output fields for troubleshooting and quality review. | AI Platform Owner | Logging schema + approved fields list | 30d prompts / 30d outputs | P0 |
| 3 | Access Controls | Limit read access to prompt and output logs by role, and enforce auditable access reviews. | Security Operations | Access control list + monthly access review record | Review monthly | P1 |
| 4 | Deletion Operations | Define deletion SLA and escalation path for legal hold, customer requests, and policy violations. | Compliance Lead | Deletion runbook + ticket SLA dashboard | Deletion SLA 8-30 days | P1 |
| 5 | Governance Review | Run recurring retention policy reviews with unresolved control gaps, incidents, and vendor changes. | AI Governance Program Owner | Governance review minutes + control action log | Monthly | P1 |
| 6 | Regulatory Controls | Map retention and deletion rules to regulatory obligations and keep evidence ready for audits. | Legal + Compliance | Regulatory mapping register + policy exception log | Review monthly | P0 |
| 7 | Redaction Controls | Require pre-storage masking for identifiers, regulated attributes, and sensitive business data. | Application Engineering | Masking tests + release gate checks | Before each release | P1 |

## Enforcement checklist
1. Run a 14-day remediation sprint for redaction, deletion SLA, and contract controls before expanding traffic.
2. Confirm one accountable owner and one evidence artifact for each P0 policy row.
3. Validate log retention settings in production match documented policy windows.
4. Review unresolved retention risks in the next governance meeting with dated actions.

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