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Bureau of AI
Category: Compliance & Audit

Record-Keeping

Also known as: Recordkeeping, Record keeping
Simply put

Record-keeping is the practice of recording and storing important information so it can be referenced later. In an organizational setting, it typically means keeping consistent, formal track of activities, transactions, and decisions. The specific information retained is usually selected for a particular purpose, such as reporting or accountability.

Formal definition

Record-keeping is the systematic act or practice of creating, capturing, and retaining selected information for future reference, commonly for a defined purpose such as documenting transactions, activities, or decisions. It may be performed manually or digitally and often reflects the maintenance of consistent, formal records of an organization's activities over time. As commonly framed in operational guidance, mature record-keeping is supported by an established policy and procedure rather than ad hoc retention; the evidence provided defines the general concept and does not address AI-specific or regulatory documentation requirements, which fall outside this entry's scope.

Why it matters

Record-keeping is a foundational practice underpinning accountability in most organizational settings. By creating and storing consistent, formal records of activities, transactions, and decisions, an organization builds a reference base that can be drawn on later for reporting, oversight, or review. Without such records, it becomes difficult to reconstruct what happened, when, and on what basis a decision was made.

The value of record-keeping depends heavily on the purpose for which information is selected and retained. As commonly framed, records are not kept indiscriminately; useful information is captured for a specific purpose. This selectivity means that the quality of record-keeping is tied to how well the retained information matches the intended use, whether that is demonstrating that activities occurred, supporting reporting obligations, or providing a basis for accountability over time.

Mature record-keeping is typically supported by an established policy and procedure rather than ad hoc retention. Treating record-keeping as a defined practice, rather than an incidental habit, helps ensure records are consistent and available when needed. Note that this entry addresses record-keeping as a general concept; AI-specific documentation obligations and regulatory retention requirements fall outside its scope and would need to be assessed against the applicable framework.

Who it's relevant to

Compliance and reporting functions
Those responsible for reporting rely on record-keeping to reference the history of an organization's activities. Consistent, formal records support the ability to report accurately, and, as noted in the evidence, developing a record-keeping policy and procedure is treated as best practice in reporting contexts such as charity reporting.
Operational and business roles
Staff involved in tracking business transactions and activities use record-keeping, whether manual or digital, to maintain a reliable account of what has occurred. This provides a reference base for later review and helps ensure that useful information is captured for its intended purpose.
Governance and oversight stakeholders
Those concerned with accountability benefit from consistent, formal records of decisions and activities maintained over time. Note, however, that this entry defines the general concept of record-keeping; stakeholders working with AI systems or under specific regulatory regimes should consult the applicable framework, as AI-specific and regulatory documentation requirements are outside the scope of this evidence.

Inside Record-Keeping

Documentation of model design and development
Records capturing the model's purpose, design choices, data sources, assumptions, limitations, and development methodology. In model risk contexts, this typically supports the ability of an independent party to understand and challenge the model.
Validation and testing evidence
Retained results of validation activities, performance testing, and outcome analysis. Note that validation (assessing whether the right model was built for its intended use) is distinct from verification (confirming the model was implemented as specified); record-keeping ideally preserves evidence of both without conflating them.
Data lineage and provenance records
Documentation tracing the origin, transformations, and quality of data used to build, train, or run a model, enabling reconstruction and review. The specific expectations vary by jurisdiction and framework.
Governance and decision records
Records of approvals, sign-offs, roles, and accountability decisions relating to a model or AI system. These reflect governance (organizational oversight structures) rather than the risk measurement activities of model risk management, though the two overlap in practice.
Monitoring and change logs
Ongoing records of performance monitoring, incidents, retraining events, and version changes over the lifecycle. These help distinguish ongoing model performance degradation from underlying model risk, which are related but not the same.
Retention and access controls
Policies specifying how long records are kept, in what form, and who may access or amend them. Required retention periods and formats differ across regulatory regimes and sectors, so specifics should be confirmed against the applicable framework.

Common questions

Answers to the questions practitioners most commonly ask about Record-Keeping.

Is record-keeping the same as model documentation?
No. These are related but distinct. Model documentation typically describes a model's design, assumptions, data, methodology, and limitations at a point in time, and is often a deliverable produced during development and validation. Record-keeping is the broader, ongoing practice of capturing and retaining evidence about an AI system's lifecycle, decisions, changes, approvals, and oversight activities. Documentation is often one input to a record-keeping regime, but record-keeping also encompasses logs, approvals, monitoring outputs, and audit trails that documentation alone may not cover. Professionals sometimes err by treating a documentation package as sufficient to satisfy record-keeping expectations, which can leave gaps in the evidentiary trail over time.
Does keeping records mean an organization is compliant or that its AI risk is controlled?
Not on its own. Record-keeping is a supporting control that provides evidence of what was done, decided, and observed; it does not by itself establish that a system is well governed, that risk has been reduced, or that any particular regulatory requirement has been met. Records demonstrate whether governance and risk activities occurred and can be reviewed, but they do not substitute for the substance of those activities. Treating the existence of records as proof of compliance or of controlled risk is a common misconception; records enable verification, they do not guarantee an outcome.
What kinds of records are commonly retained for AI systems?
Practices vary by organization, sector, and applicable framework, but records commonly maintained include design and development documentation, data sourcing and preparation details, validation and testing results, approval and sign-off evidence, change and version history, monitoring and performance outputs, and records of oversight or governance decisions. In some regulated settings, additional operational logs may be expected. The specific set depends on the framework you are operating under and the risk profile of the system, so organizations typically map their record-keeping to their own policies and any applicable obligations rather than to a single universal checklist.
How long should AI-related records be retained?
There is no single universal retention period. Retention is typically driven by a combination of internal policy, sector-specific requirements, contractual obligations, litigation-hold considerations, and any applicable legal or regulatory expectations in the relevant jurisdiction. Organizations commonly define retention schedules by record type and risk tier. Because obligations differ across jurisdictions and sectors, and because some requirements are evolving, firms generally consult legal and compliance functions to set defensible retention periods rather than applying a default figure across all records.
Who is responsible for maintaining AI records within an organization?
Responsibility is usually distributed across roles rather than assigned to a single owner. In many governance structures, model owners or developers (often associated with the first line of defense) generate development and operational records, while independent validation or risk functions (often the second line) maintain review and challenge evidence, and internal audit (often the third line) may assess whether record-keeping is adequate. Clear assignment of ownership for each record type is a common practice, since ambiguous responsibility is a frequent source of gaps. The exact allocation depends on the organization's operating model and governance framework.
How can record-keeping be made reliable and auditable in practice?
Common approaches include defining what must be captured for each stage of the AI lifecycle, standardizing formats and storage locations, capturing version and change history, and ensuring records are protected against unauthorized alteration so their integrity can be relied upon during review. Linking records to approvals, monitoring outputs, and oversight decisions helps establish a traceable trail. Many organizations also periodically review whether records are complete, retrievable, and consistent with policy. These measures support auditability and reduce the risk of evidentiary gaps, though they do not by themselves guarantee that any particular requirement is satisfied.

Common misconceptions

Record-keeping is simply archiving files after a model is built.
In many frameworks, record-keeping is a continuous lifecycle activity intended to support independent review, challenge, and reproducibility over time, not a one-time storage task performed at deployment.
One record-keeping standard satisfies all obligations.
Expectations differ by jurisdiction and instrument. Supervisory guidance for banking model risk, voluntary standards, and statutory regimes each frame documentation differently, and they are not interchangeable. Practitioners should scope requirements to the frameworks that actually apply to them.
Thorough record-keeping demonstrates that a model is low risk or compliant.
Documentation evidences and supports governance and risk-management processes; it does not by itself reduce a model's inherent risk or guarantee compliance. It is a control that helps manage and evidence risk, not one that eliminates it.

Best practices

Maintain records across the full model lifecycle rather than only at development or deployment, capturing design, validation, monitoring, and change events as they occur.
Keep validation evidence and verification evidence clearly labeled and separable, so reviewers can see both whether the right model was built and whether it was implemented correctly.
Scope retention periods, formats, and access controls to the specific frameworks and jurisdictions that apply to your organization, and confirm requirements rather than assuming a single universal standard.
Preserve data lineage and provenance sufficient to reconstruct how a model was built and run, supporting independent review and challenge.
Distinguish governance decision records (approvals, roles, accountability) from risk-measurement records (testing, monitoring outcomes), while documenting where they intersect.
Establish version control and change logs so that model updates, retraining, and incidents are traceable over time and can be tied to specific approvals.