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There is no universal retention period for AI records. Set a documented schedule based on the system’s risk and legal classification, the purpose of each record, post-market monitoring needs, privacy obligations, contracts, and the chance of an incident or dispute. For an EU high-risk AI system, Article 19 of Regulation (EU) 2024/1689 sets a floor of six months for covered logs, while Article 18 requires specified technical and conformity documentation to remain available for 10 years. Those figures are not a general rule for every AI system.
Keep each released model version traceably connected to the tests, data, metrics, approvals, incidents, and changes that justify its use. Retain high-value evidence for as long as it can be needed to demonstrate safety, reproduce a decision, satisfy a contract or regulator, investigate an incident, or defend litigation. When the purpose ends, delete or anonymize personal data securely unless a valid hold or longer obligation applies.
Start by separating the records you are retaining
“AI logs” can mean several different things. A retention policy that gives one period to all of them either destroys evidence too soon or stores unnecessary personal data indefinitely. Classify records before assigning a schedule.
Runtime and event logs
These are automatically generated records of production events: requests, responses or decisions, timestamps, model and software identifiers, safety interventions, errors, and other events needed to identify risk or monitor the system after deployment. They can contain user prompts, uploaded files, identifiers, or inferred information, so they are often personal-data stores.
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Test, evaluation, validation and verification evidence
TEVV evidence shows how a model or system was assessed before and after release. It includes test-set and dataset versions, validation procedures, evaluator identity, metrics, failure cases, approvals, and signed test reports. A summary dashboard is not a substitute for the underlying controlled evidence needed to reproduce a release decision.
Model-version and release records
A release record identifies the exact model artifact and its environment. It normally includes a model identifier and hash, provider and version, prompt and configuration, code and dependency versions, dataset or test-set version, evaluator, timestamp, environment, metrics, failures, approvals, incidents, remediation links, and a history of material changes.
Technical, quality and conformity documentation
This is the controlled documentation used to demonstrate that a regulated system was designed, tested, operated, and changed under an approved process. It can include the technical file, quality-management records, conformity documents, and records from a notified body where one is involved. These records have a different retention rationale from high-volume production payloads.
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The EU rules provide concrete minimums for certain high-risk systems, but they do not create a single retention period for all AI. Applicability depends on the system’s role, the provider or deployer’s obligations, and any other Union or national law, including data-protection law.
| Requirement | What must be retained | Period or rule | Source |
|---|---|---|---|
| Automatic event recording | Events (logs) that support risk identification, detection of substantial changes, and post-market monitoring | High-risk systems must technically allow recording over the system lifetime | Regulation (EU) 2024/1689, Article 12 |
| Provider log retention | Logs generated automatically by a high-risk system | A period appropriate to the intended purpose, at least six months, unless other Union or national law applies | Regulation (EU) 2024/1689, Article 19 |
| Technical and conformity documentation | Technical documentation, quality-management documentation, applicable notified-body records, and the EU declaration of conformity | Available for 10 years after the system is placed on the market or put into service | Regulation (EU) 2024/1689, Article 18 |
| Technical documentation contents | System and software versions, relationships to earlier versions, validation and testing procedures, test-data characteristics, metrics, dated and signed test logs and reports, and lifecycle changes | Retained as part of the documentation to which the Article 18 availability period applies, where the Annex IV requirements cover the system | Regulation (EU) 2024/1689, Annex IV |
Article 12 says that “High-risk AI systems shall technically allow for the automatic recording of events (logs) over the lifetime of the system.” Article 19 then requires the automatically generated logs to be kept for a period appropriate to the intended purpose, “of at least six months.” The six-month figure is therefore a floor for the covered high-risk logs, not a universal ceiling, default, or permission to delete other evidence.
Annex IV is especially important for test evidence. If test logs and signed reports form part of the high-risk system’s technical documentation, they should be managed with that controlled file rather than treated as disposable runtime telemetry. Confirm the exact obligation for your role and system with qualified regulatory counsel, particularly where another sector rule or national law changes the result.
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How to choose a defensible period
Use a written, risk- and purpose-based decision rather than picking a convenient number. Document the owner, record category, start event for the retention clock, deletion trigger, and exceptions.
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1. Classify the system and its risk
Record whether the system is high-risk under the EU AI Act or subject to another regulated regime. A safety-critical, employment, credit, health, critical-infrastructure, or public-sector use generally creates greater evidence and monitoring needs than an internal low-impact experiment. Classification can change after a new use, integration, or model capability is introduced.
2. State the purpose of each record
Write down what the record must prove: a release was safe, a decision can be explained, a post-market trend can be detected, a contract was met, a complaint can be investigated, or a legal claim can be defended. Retain the record for the period that purpose reasonably requires. If a purpose ends, keeping identifiable data without another lawful purpose becomes harder to justify.
3. Check external obligations
Map legislation, regulator guidance, customer contracts, insurance conditions, sector standards, and internal security requirements. A contract may require evidence for a defined term; a sector rule may require longer availability; a privacy rule may require earlier erasure. Apply the obligation that governs the particular record and jurisdiction, not the longest period indiscriminately.
4. Assess reproducibility value
Ask whether an investigator could recreate the result from the retained material. A model hash without its configuration, dependency versions, test-set identity, and environment is weak evidence. Conversely, retaining every raw prompt forever may add little reproducibility value while increasing privacy and breach exposure. Preserve the smallest set that can support the stated purpose.
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Estimate how long a safety issue, customer complaint, employment claim, product-liability allegation, or regulator inquiry could arise. Once an incident, complaint, investigation, audit, or reasonably anticipated dispute exists, suspend routine deletion for relevant records. The hold should identify the scope, approver, date, and reason.
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6. Price storage and access risk
Retention has two costs: storage and the risk created by holding sensitive data. Keep immutable, frequently accessed evidence in a controlled store; move older records to access-controlled archival storage; and keep only what is necessary. Cost is a factor in designing the schedule, but it does not override a legal hold or mandatory availability period.
A practical retention schedule
The following pattern gives each record a separate rule. Replace the trigger and period with the result of your legal, privacy, contractual, and risk assessment.
| Record category | Keep until | Controls and notes |
|---|---|---|
| High-risk automatic event logs | The Article 19 purpose-based period, never below the applicable six-month floor, unless another law requires more | Capture model and software identifiers, time, event type, and integrity data; limit or tokenize personal content |
| Raw operational prompts, outputs, and feedback | The shortest period that supports monitoring, support, safety investigation, or a documented contract | Define sampling, redaction, access, and deletion; treat inputs and feedback as potential personal data |
| TEVV datasets, procedures, metrics, and signed reports | The applicable safety, regulatory, contractual, and litigation period | Version and hash datasets; preserve evaluator, date, environment, failures, and approvals |
| Model-version release package | While the version can matter to a deployed decision, safety inquiry, contract, regulator, or claim | Keep old versions linked to the exact tests and changes that authorized deployment |
| EU high-risk technical and conformity file | The Article 18 availability period where applicable: 10 years after placing on the market or putting into service | Protect against alteration; maintain version history and retrieval capability |
| Incident, complaint, audit, and legal-hold records | Until the investigation, applicable claim period, audit requirement, or hold is formally closed | Do not allow an automated deletion job to remove held material |
“Until” is deliberate: outside the stated EU figures, a responsible period depends on the use and obligations rather than an invented universal number.
What to archive for every material model release
Create one immutable evidence bundle for each release that can affect users, safety, compliance, or contractual performance. Give it a stable release identifier and cryptographic hash, and record the following:
- Model name, provider, exact version, artifact hash, and predecessor or successor relationship.
- Prompt templates, system instructions, inference parameters, safety settings, and other configuration that changes behavior.
- Application code, libraries, firmware where relevant, dependency lockfiles, infrastructure or container image identifiers, and the execution environment.
- Training, fine-tuning, validation, and test-set identifiers; data characteristics; provenance; licensing or usage restrictions; and known limitations.
- Test procedures, evaluator identity, dates, signed reports, performance metrics, thresholds, confidence or uncertainty information where used, and representative failures.
- Risk assessment, approval decision, change description, rollback version, monitoring plan, and links to remediation tickets.
- Deployment date, regions or customer populations, interfaces, access controls, and the retention classification assigned to associated runtime logs.
Store the bundle so later edits create a new version rather than silently changing the original. Restrict write access, log reads and exports, back up across failure domains, and test that an auditor can retrieve a complete bundle without relying on an engineer’s local workstation.
Model versions: archive them, but do not keep everything forever
A superseded model should not disappear merely because production traffic moved to a newer version. Keep the retired version’s identifier, hash, configuration, test evidence, approvals, deployment dates, observed incidents, and change history for as long as a decision made by that version could need investigation. This is what lets an organization explain which model produced an output and whether it was operating within the approved envelope.
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Archiving does not require keeping every copy of every binary in primary storage. A controlled, integrity-checked archive can preserve the artifact and reproducibility metadata while older runtime payloads are sampled, aggregated, redacted, or deleted under their own schedule. If a provider’s terms prevent retaining the artifact, preserve the strongest available evidence—immutable identifiers, provider/version information, test results, configuration, and contractual records—and document the limitation.
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Training data, validation data, operational inputs, user feedback, and data embedded in a model can all contain personal information. EDPB training material treats these as stores requiring explicit retention decisions and secure-erasure controls. A compliance file does not automatically justify indefinite retention of identifiable content.
- Separate identity and contact fields from test evidence wherever possible.
- Redact, tokenize, aggregate, or sample prompts and outputs when full payloads are not necessary.
- Keep a documented purpose and lawful basis for each personal-data store, with access limited to the people who need it.
- Define how backups, caches, exports, analyst notebooks, and vendor copies are included in deletion.
- At expiry, securely erase or anonymize the data, revoke access, and destroy encryption keys where appropriate.
- Retain a deletion record—what category was deleted, when, by whom or which controlled process, and under which rule—without retaining the personal content itself.
Anonymization must be assessed for re-identification risk; simply removing a name may not be enough. If a legal or incident hold applies, record the hold and preserve only the relevant scope until an authorized release.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Implement the policy step by step
- Inventory the stores. List production log streams, test repositories, model registries, datasets, ticketing systems, notebooks, backups, vendor platforms, and conformity files.
- Assign an owner and classification. For every store, name the business, engineering, privacy, and compliance owner; identify personal-data sensitivity and regulatory status.
- Define the clock. Specify whether retention starts at event time, release, retirement, market placement, contract end, incident closure, or another documented trigger.
- Write the schedule and exceptions. State the purpose, period, deletion method, legal-hold process, and who can approve an extension.
- Automate controls. Apply tags and expiry rules to new records, but require a hold check before deletion. Keep immutable audit trails for changes to the policy and schedules.
- Test retrieval and deletion. Periodically restore a release bundle, verify hashes and signatures, and run a deletion exercise that includes replicas and backups.
- Review after change. Reassess the schedule after an incident, substantial model or use change, new jurisdiction, new contract, or a change in post-market-monitoring obligations.
How major guidance fits together
NIST AI 600-1 recommends a document-retention policy that preserves history for testing, evaluation, validation, and verification, and says AI inventories should identify underlying foundation models and versions, provenance, and known issues. It is a governance framework, not a blanket statutory period.
ISO/IEC 42001 implementation guidance similarly frames event-log retention around the intended use of the AI system and the organization’s data-retention policies. Neither replaces a law, contract, or privacy assessment; both support a controlled, auditable process.
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Common mistakes and how to correct them
Treating six months as the answer for every AI system
That number is an EU high-risk log floor under a specific article. Correct the policy by classifying the system and setting a purpose-based period for each record type.
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Keeping only a model filename
A filename cannot prove which artifact, configuration, data, or test justified deployment. Add hashes, versions, environment, metrics, approvals, and change links to a release bundle.
Keeping raw prompts indefinitely “for safety”
Indefinite raw retention increases privacy and security exposure. Use sampling, redaction, aggregation, and reproducibility metadata, with a documented exception for incidents or holds.
Deleting a retired model and its tests together
Retirement ends routine use, not necessarily the need to explain past decisions. Preserve the version’s evidence for the applicable safety, regulatory, contractual, and claim period.
Letting backups bypass the schedule
Map replicas, snapshots, exports, caches, and supplier copies. Define how they expire or become inaccessible and document any technically unavoidable residual copies.
Bottom line
Retain AI test evidence and model versions for as long as they are needed to prove safety, traceability, reproducibility, monitoring, contractual compliance, or legal defensibility. For EU high-risk systems, apply the six-month minimum to covered automatically generated logs and the 10-year availability rule to applicable technical and conformity documentation. Keep personal data only for a defined purpose, place incident and legal holds before deletion, and review the schedule whenever the model, use, jurisdiction, or obligations change.
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