Keep a controlled, retrievable record set for each AI system, from approval and design through operation, change, and retirement. It should capture the system’s purpose and owners, applicable requirements, data and model documentation, risk decisions, tests, monitoring, incidents, and changes. This is a practical governance baseline—not a universal legal checklist: first establish your jurisdiction, sector, role, system classification, intended use, and applicable privacy and records laws.
What records should organizations keep for AI compliance?
Organize evidence around each system and its lifecycle, rather than treating runtime logs as the entire compliance file. A useful record set makes it possible to answer four questions: what the system is for, who is accountable for it, how its risks were assessed and managed, and what happened after deployment.
The categories below are a practical cross-framework baseline. Which items are legally required, who must keep them, and for how long depends on the applicable rules and the organization’s role. Distinguish mandatory records from internal governance records in your own schedule.
System inventory and ownership
- System and model name, version, deployment status, purpose, intended users, and operating context.
- Business owner, technical contacts, risk owner, and whether the organization acts as provider, deployer, or in another role.
- Major components, third-party services or dependencies, and relevant documentation locations.
- Planned review dates and, when applicable, retirement date.
Applicable requirements and accountability
- Analysis of relevant jurisdictions, sector rules, system category, and intended use.
- Applicable laws, internal policies, and the decisions made about how they apply.
- Assigned decision rights, named owners, approvals, exceptions, and review cadence.
Design, data, and model evidence
- System characteristics, capabilities, limitations, and relevant technical documentation.
- Algorithms and material system dependencies, where relevant to understanding the system.
- Data sources and characteristics, plus the training and validation approach where applicable.
- Information needed to explain how the system was developed and to assess compliance with applicable requirements.
Risk and impact records
- Intended-use context, expected benefits, foreseeable harms, and affected people or groups.
- Risk assessments, mitigation decisions, residual risks, and the rationale for accepting or escalating them.
- Human oversight arrangements and material limitations communicated to users or downstream parties.
Testing and validation
- Evaluation plans, methods, metrics, and test-data provenance where appropriate.
- Results, uncertainty, known limitations, and approval decisions.
- Repeat evaluations after meaningful changes and at suitable points during operation.
Operation, monitoring, and incidents
- Release history and deployment configuration, including material updates.
- Monitoring measures and results, performance changes, relevant user feedback, and oversight or access records.
- Incident reports, investigations, escalations, and required communications with regulators or customers.
- Mitigation, rollback, and corrective-action decisions, with evidence of follow-up.
Incident reportability and retention are determined by the applicable rules; a general AI record checklist does not establish either.
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- Change requests, approvals, impact reassessments, and resulting release records.
- End-of-life decisions and decommissioning steps.
- Record disposition decisions made under the organization’s retention schedule, including any legal hold.
What records and logs does the EU AI Act require for high-risk AI systems?
The EU AI Act has specific obligations for high-risk AI systems; they should not be generalized to every AI use or every organization. The applicable duty depends on the organization’s role and the system’s classification.
Automatic event logging capability
Article 12 requires high-risk AI systems to technically allow automatic recording of events over the system’s lifetime. The logging capability is intended to capture events relevant to identifying risks or substantial modifications, post-market monitoring, and operational monitoring.
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For the Annex III point 1(a) remote biometric identification systems, the Act specifies minimum log information that includes each use period, the reference database, input data that produced a match, and the identities of people who verified results.
Provider documentation retention
Under Article 18, providers of high-risk AI systems must keep specified materials available to national competent authorities for ten years after the system is placed on the market or put into service. The specified materials include technical documentation, quality management system documentation, applicable notified-body change approvals and decisions, and the EU declaration of conformity.
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If a provider or its authorized representative ceases activity before the period ends, Member States determine the arrangements. Financial-institution providers maintain the documentation within the relevant Union financial-services documentation framework.
Provider-controlled log retention
Under Article 19, providers keep automatically generated Article 12 logs to the extent those logs are under their control, for an appropriate period of at least six months, unless applicable Union or national law provides otherwise—particularly data-protection law. The consolidated regulation also contains deployer log obligations; organizations should check the provision that applies to their role and context.
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| Record type | Who and what | Retention rule stated in the Act |
|---|---|---|
| Specified documentation | High-risk AI providers: technical documentation, quality management system documentation, applicable notified-body change approvals and decisions, and EU declaration of conformity | Ten years after placing on the market or putting into service |
| Automatically generated logs | High-risk AI providers, to the extent Article 12 logs are under their control | An appropriate period of at least six months, subject to applicable Union or national law |
These are distinct obligations: the ten-year period applies to the listed provider documentation, not to every AI record or every log. Other applicable laws may add duties or change the retention outcome.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How long do we need to keep AI system logs?
There is no universal retention period for all AI logs. Set a schedule by record category and applicable law, taking account of jurisdiction, sector, system classification, organizational role, log control, and legal exceptions. For the EU AI Act provider obligations above, the stated baseline for controlled automatically generated logs is an appropriate period of at least six months, subject to applicable law. Do not apply the separate ten-year provider-documentation period to logs by default.
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How should an organization make its AI records usable?
Records help only if the people responsible can find, interpret, protect, and produce them. Establish ownership and controls alongside the record categories.
- Assign a record owner for each system. Name the person accountable for keeping the inventory current and coordinating evidence from business, technical, risk, privacy, and legal teams.
- Use a system-linked repository. Keep the inventory, supporting documents, approvals, test evidence, and relevant log locations connected by a stable system identifier and version.
- Apply access and integrity controls. Restrict access to appropriate roles, preserve changes to records, and maintain backups and secure storage.
- Define retention by record category. Record the applicable legal basis, trigger date, retention period, exceptions, and disposition process; preserve records subject to a legal hold.
- Make retrieval part of operations. Test whether the organization can promptly assemble the relevant evidence for an audit, incident investigation, or other authorized request.
Can NIST AI RMF help structure the record set?
Yes, as voluntary guidance. NIST AI RMF 1.0 organizes risk-management outcomes into Govern, Map, Measure, and Manage. Its Core and Playbook can help teams structure documentation of policies, inventories, roles, context, risk decisions, measurement, monitoring, and repository practices.
NIST describes the AI RMF as voluntary, and the Playbook’s suggested actions are neither a checklist nor an ordered set of steps. It can inform an organization’s operating approach, but it does not replace legal analysis for the organization’s jurisdiction, sector, role, and system.
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