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Organizations should keep a controlled, retrievable record set for each AI system, covering its purpose and owners, applicable requirements, data and model documentation, risk decisions, testing, operation, incidents, changes and retirement. There is no universal AI retention period or statutory checklist: the records and deadlines depend on jurisdiction, sector, the organization’s role and the system’s risk category. The EU AI Act sets specific duties for certain high-risk AI systems; the NIST AI Risk Management Framework (AI RMF) offers voluntary guidance for structuring a broader governance program.
Start with the system and its legal context
Organize evidence around each AI system and its lifecycle, not just around the team that built it or a folder of runtime logs. First establish where the system is used, what it is intended to do, who provides and deploys it, which risk category applies, and which privacy, sector, employment, consumer, records-management or other rules may govern it. These factors determine which records are legally required and how long they must be kept.
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Use the categories below as a practical governance baseline. They are not, by themselves, a universal legal checklist. Mark each record as a legal requirement, an internal policy requirement or a governance practice, and identify the rule or decision that establishes its status.
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System inventory and ownership
Maintain a current inventory entry for each system, including its name and version, purpose, intended users, deployment context, status and planned or actual retirement date. Identify the business owner, technical contacts and relevant provider, deployer or other operator roles. Record major components and third-party dependencies so reviewers can see what the system comprises and who is responsible for each part.
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Where useful, link the entry to system documentation, data dictionaries, incident-response plans, source-code references and contacts for other organizations involved in the system. The inventory should make it possible to locate the evidence and accountable people for a specific deployed version.
Applicable requirements and accountability
Keep the analysis of relevant jurisdictions, sectors, legal duties and organizational policies, together with the decisions made about applicability. Record named system and risk owners, assigned decision rights, approvals, exceptions and the cadence for review. NIST’s AI RMF Playbook recommends documenting legal and regulatory requirements involving AI, but the framework is voluntary; it does not determine which laws apply to a particular organization.
Design, data and model documentation
Preserve current technical documentation describing system characteristics, capabilities and limitations; algorithms or model approach; relevant data sources and characteristics; training and validation methods; and material dependencies. Record enough context to explain what the system is designed to do and how its evidence relates to the version in use. For high-risk AI under the EU AI Act, technical documentation is among the specified provider records subject to the Act’s retention rule discussed below.
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Risk and impact assessments
Document the use context and intended purpose, expected benefits, potential harms and affected people or groups. Keep identified risks, treatment decisions, residual risks and the rationale for accepting or escalating them. Include human oversight arrangements and material limitations communicated to downstream users. Revisit the assessment when the intended use, deployment context or system changes in a way that may affect risk.
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Testing and validation
Keep evaluation plans, methods, metrics, relevant test-data provenance, results, limitations and uncertainty, along with approvals and decisions about whether results support deployment. Record repeat evaluations after meaningful changes. NIST’s AI RMF describes measurement as including documented functionality and trustworthiness, formalized reporting, and testing before deployment and regularly during operation.
Deployment, monitoring and operational evidence
Maintain release and version history, deployment configuration, monitoring measures and results, performance changes, user feedback, and records of access or human oversight where relevant. Connect monitoring evidence to the system version and operating context it describes. The EU AI Act requires high-risk systems to technically allow automatic recording of events over their lifetime; NIST also recommends monitoring systems in production.
Incidents and corrective action
Keep incident reports, investigations, escalations, mitigation or rollback decisions and follow-up evidence. Record regulator, customer or other external communications when required. Whether an event is reportable, who must report it and how long related records must be retained depend on the applicable rules; a general AI record checklist cannot establish those duties.
Changes and retirement
Preserve change requests and approvals, impact reassessments, release records and end-of-life decisions. Document decommissioning steps and how records are disposed of or retained under the applicable schedule. NIST’s AI RMF treats risk management as a lifecycle activity, including safe decommissioning.
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Retention, security and retrieval
Assign retention rules by record category and applicable legal requirement rather than applying one blanket period to every AI artifact. Use secure, organized repositories with access controls and integrity protections; maintain backups and apply legal holds when needed. A record program is useful only if authorized staff can retrieve reliable evidence for an audit, investigation or incident response.
What records and logs does the EU AI Act require for high-risk AI systems?
The EU AI Act distinguishes automatic logging capability from keeping particular records. Article 12 requires high-risk AI systems to technically allow automatic recording of events (logs) over the system’s lifetime. Logs should capture events relevant to identifying risks or substantial modifications, post-market monitoring and operational monitoring.
For the specified Annex III point 1(a) systems—remote biometric identification—the minimum log information includes each use period, the reference database against which inputs were checked, input data that produced a match, and the identities of people who verified results. This specific minimum should not be generalized to every high-risk system.
Article 18 separately requires providers to keep specified documentation available to national competent authorities for ten years after the system is placed on the market or put into service. The list includes technical documentation, quality-management-system documentation, applicable notified-body change approvals and decisions, and the EU declaration of conformity. If a provider or its authorized representative ceases activity before the period ends, Member States determine the arrangements. Financial-institution providers maintain documentation within the relevant Union financial-services documentation framework.
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Article 19 concerns provider-controlled logs generated under Article 12: providers must keep them for an appropriate period of at least six months, unless applicable Union or national law provides otherwise, particularly data-protection law. The ten-year Article 18 period is not a blanket retention rule for logs or for every AI record. The provisions described here are specific to the EU AI Act’s scope and should not be treated as obligations for every AI use or jurisdiction.
How long do organizations need to keep AI system logs?
There is no single period that applies to all organizations or AI logs. Under the EU AI Act, the provider’s controlled Article 12 logs for high-risk systems have an appropriate-period requirement with a minimum of six months under Article 19, subject to applicable Union or national law. The separate ten-year Article 18 period applies to the specified provider documentation, measured from placement on the market or putting into service.
| EU AI Act record | Who and what | Period stated in the Act |
|---|---|---|
| Article 18 documentation | Providers of high-risk AI systems; specified technical and quality-management documentation, applicable notified-body approvals and decisions, and the EU declaration of conformity. | Ten years after placement on the market or putting into service. |
| Article 19 logs | Providers of high-risk AI systems; automatically generated Article 12 logs to the extent under the provider’s control. | An appropriate period of at least six months, except where applicable Union or national law provides otherwise. |
These periods are stated in the EU AI Act consolidated text dated 27 July 2026. Other legal duties may add to or change what a particular organization must retain. Confirm the applicable rules for the system’s role, use and jurisdiction before setting a schedule.
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How NIST AI RMF can help organize the records
NIST AI RMF 1.0 is a voluntary framework, not a law or a universal checklist. Its four functions—Govern, Map, Measure and Manage—offer a way to organize evidence across the AI lifecycle:
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- Govern: capture policies, accountable roles, decision rights and repository practices.
- Map: document the system’s context, intended use, affected parties and identified risks.
- Measure: preserve evaluation methods, results, limitations and reporting.
- Manage: record risk responses, monitoring, corrective action and decommissioning decisions.
The Playbook suggests actions that can support this work, including system inventories, documentation responsibilities, organized repositories, risk responses, measurement and monitoring. NIST expressly describes those actions as voluntary and says the Playbook is neither a checklist nor an ordered set of steps. Use it to structure a program, not as a substitute for legal analysis.
What documentation should go in an AI system inventory?
At minimum, make each entry identify the system and version, its owner, provider/deployer status, intended purpose and users, deployment context, components and third-party dependencies, current status and retirement date. Link to the relevant technical documentation, risk assessment, test results, incident-response plan and operational evidence where those records are maintained separately. This lets the inventory function as a map to the compliance file rather than a duplicate archive.
Set an owner for keeping the entry current when the system changes, is redeployed for a different use, or is retired. NIST’s inventory guidance also points to documenting contacts and related artifacts such as data dictionaries and incident-response plans.
Build a usable record program
- Scope the system: establish jurisdiction, sector, intended use, organizational role and risk classification before deciding which duties apply.
- Assign ownership: name the business and risk owners, technical contacts and people authorized to approve or accept risks.
- Map evidence to decisions: connect each risk, approval and compliance requirement to the record that supports it and to the system version it concerns.
- Set category-specific retention: document the legal or policy basis and trigger date for each schedule; do not infer a universal period from the EU AI Act’s scoped rules.
- Control access and retrieval: keep records in organized repositories with appropriate protections, backups and a way to retrieve evidence promptly.
- Review through the lifecycle: update evidence for material changes, incidents, monitoring outcomes and decommissioning.
The result should be a traceable account of what the system is, why it is used, who is accountable, what risks were considered, what evidence supported decisions, and how the system was operated and changed. The applicable law—not a generic checklist—sets which of those records are mandatory and the retention period that applies.
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