---
title: "Data retention policies for AI memory"
description: "A data retention policy states how long an AI system keeps each class of stored information and what happens at expiry. How to classify, enforce, and verify."
canonical: https://past.dev/guides/ai-data-retention
last-updated: 2026-10-17
---
# Data retention policies for AI memory

Source: https://past.dev/guides/ai-data-retention

A data retention policy for AI memory states how long each class of stored information is kept and what happens when the period ends. Retention differs from supersession: supersession keeps old values with closed validity windows so history stays queryable, while retention removes data entirely on a schedule. A workable policy classifies what the agent stores, sets a horizon per class, and is enforced by scheduled deletion rather than by intention.

## Retention, supersession, and erasure are different controls

| Control | Trigger | What remains afterwards |
| --- | --- | --- |
| Supersession | A fact changes | Both values, with dated validity windows |
| Retention expiry | A class's time horizon passes | Nothing from the expired sources |
| Erasure | A person or obligation demands it | Nothing that only those sources supported |
| Eviction or decay | Relevance ranking demotes it | The data, ranked lower at recall |

Confusing these controls produces real failures. Supersession used where retention is required keeps data that should be gone. Retention used where supersession is wanted deletes history the [audit trail](/guides/ai-audit-trail) depends on. The four are defined side by side in [designing a forgetting policy](/guides/forgetting-policy).

## Classifying what an agent stores

A single horizon for everything is either too short for operational facts or too long for personal data. Classification comes first.

- **Raw source records.** Transcripts, emails, tickets. Often the largest class and the one with the clearest legal constraints.
- **Extracted facts about the business.** Budgets, owners, deadlines. Usually kept long, with supersession carrying the history.
- **Personal data and preferences.** Subject to privacy law and to [erasure requests](/guides/gdpr-memory-deletion) regardless of the schedule.
- **Operational records.** Recall logs kept as audit evidence; for some deployments a legal minimum applies, see [EU AI Act record-keeping](/guides/eu-ai-act-record-keeping).

## Enforcement mechanics

Enforce retention by keeping the source id and ingestion id needed for deletion. The verification loop is the same one used for [erasure](/guides/gdpr-memory-deletion): recall before deletion, delete the ingestion, then recall again and confirm the removed source is absent while independently supported memory remains.

With Bring your own cloud, stored memory stays in the customer's own database or cloud account; see [deployment options](/enterprise). Export before expiry is available in every deployment: customers can export their data.

## Writing the policy

1. Inventory the classes above and map each thing the agent ingests to one class.
2. Set a horizon per class, with the reasons written down: legal minimums, legal maximums, and operational need.
3. Decide per class whether history is kept by supersession or removed at expiry.
4. Name who may trigger out-of-schedule erasure and how requests are validated.
5. Automate enforcement and alert on failures; a retention job that silently stops is a liability.
6. Test quarterly with the recall-delete-recall loop, and record the runs as audit evidence.

## Where past.dev fits

past.dev gives retention its enforcement primitive: `DELETE /api/v1/ingest/{ingestionId}` removes a source and erases what only it supported. Stable source identifiers assigned at ingestion make the expiry job a loop over known ids; the [quickstart](/docs/memory-api/quickstart) shows where they are set. Recall behavior on questions whose support has been removed is part of what the [benchmarks](/benchmarks) evaluate.

## Frequently asked questions

### How long should an AI agent keep conversation history?

As long as the class it belongs to justifies, and no longer. Transcripts holding personal data usually get a short horizon; durable business facts extracted from them can be kept with supersession carrying the history.

### Does retention apply to facts derived from expired sources?

Yes. Deletion by source erases the facts that only the expired sources supported. A fact that other, unexpired sources also support remains with its surviving evidence.

### Is a retention policy legally required?

Some regimes set minimums for specific records and others effectively set maximums for personal data. The operator of the agent owns the analysis; the memory layer's job is to make whatever policy they choose enforceable and verifiable.

## Related

- [Designing a forgetting policy](https://past.dev/guides/forgetting-policy)
- [The right to be forgotten in agent memory](https://past.dev/guides/gdpr-memory-deletion)
- [Audit trail for AI agent decisions](https://past.dev/guides/ai-audit-trail)
- [EU AI Act record-keeping](https://past.dev/guides/eu-ai-act-record-keeping)
- [Deployment options](https://past.dev/enterprise)