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Method · Evidence management

AI governance evidence register

A policy statement is not evidence that a live agentic system is controlled. The evidence register creates traceability from a selected requirement or governance objective to the technical and operational artifact that demonstrates implementation and current review status.

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Direct answer

What is an AI governance evidence register?

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It is a versioned index that links a requirement, objective, risk, or policy statement to its scope, owner, control, evidence artifact, storage location, test or review method, finding, exception, approval, and next review date. It supports inspection and change management; it does not itself establish legal compliance, certification, or control effectiveness.

  • Use one row per evidence assertion rather than one row per entire framework.
  • Record source version and reviewed date.
  • Link to the artifact and test result, not merely the policy that requires it.

Source basis: reviewed official references are listed on this page and in the source ledger. Interpretation and implementation guidance retain the stated assurance boundary.

Register design

Fields that preserve traceability

The record should answer who, what, where, when, how, and with what result.

Field groupPurposeExample
SourceIdentify framework, policy, contract, internal standard, version, and clause or outcome.NIST AI RMF 1.0 · MEASURE 2.5
ScopeConnect the requirement to a system, workflow, environment, data class, and business owner.Claims triage agent · production · personal data
ControlDescribe the preventive, detective, responsive, or recovery mechanism.Tool arguments require schema validation and payment-limit policy
EvidenceIdentify artifact type, URI, version, custodian, creation date, and integrity information.Evaluation report v1.8 · evidence repository · SHA-256
VerificationDescribe test, sample, reviewer, date, result, and limitations.250 scenario release suite · 2026-07-28 · conditional pass
Finding and exceptionRecord gap, severity, compensating control, risk owner, due date, and decision.Rare escalation timeout · medium · release conditional
LifecycleSet next review and triggers such as model, prompt, data, tool, protocol, or policy change.Review quarterly or on model/tool version change

Operating method

Maintain evidence as the system changes

A static spreadsheet becomes stale unless it is part of release and incident work.

  1. Select the source and scope

    Name the exact requirement or objective, version, system boundary, and reviewer.

  2. Map the control

    Describe how the architecture, process, or contract addresses the requirement and who owns it.

  3. Attach inspectable evidence

    Link to current documents, configuration, traces, test results, approvals, incidents, or training records.

  4. Verify sufficiency

    Record the review method, sample, result, limitations, and unresolved questions.

  5. Manage exceptions

    Assign gaps and residual risk to a named owner with a date and explicit decision.

  6. Refresh on triggers

    Re-open evidence after changes, incidents, complaints, new obligations, or scheduled review.

Editable evidence record

Download the AI governance evidence register

Adapt the CSV to the organization’s selected framework, contract, policy, and evidence repository.

CSV

AI Governance Evidence Register

CSV fields for source, scope, control, artifact, verification, finding, approval, and review lifecycle.

Download CSV

Templates are planning aids. They are not certifications, legal advice, security guarantees, or substitutes for client-specific validation.

Primary-source basis

Official source ledger

Each record includes publisher, source type, status, reviewed date, summary, and use boundary.

National Institute of Standards and Technology · Official Framework Hub

NIST AI Risk Management Framework

NIST's voluntary framework for incorporating trustworthiness considerations into the design, development, use, and evaluation of AI systems.

Status
Current With Revision Underway
Published
2023-01-26
Reviewed
2026-08-01

Use boundary: Use as risk-management guidance; do not describe alignment as certification or legal compliance.

Open official source

National Institute of Standards and Technology · Official Playbook

NIST AI RMF Playbook

Suggested actions for achieving AI RMF Core outcomes, organized around Govern, Map, Measure, and Manage.

Status
Current Pending Framework Revision
Published
2023-03-30
Reviewed
2026-08-01

Use boundary: NIST states that the Playbook is not a checklist to be followed in its entirety.

Open official source

International Organization for Standardization · Official Standard Page

ISO/IEC 42001:2023 — AI management systems

Requirements and guidance for establishing, implementing, maintaining, and continually improving an AI management system within an organization.

Status
Current
Published
2023-12
Reviewed
2026-08-01

Use boundary: Do not claim certification unless an accredited audit and certification process has been completed.

Open official source

International Organization for Standardization · Official Explainer

ISO 42001 explained

An official overview of ISO/IEC 42001 requirements, including leadership, policy, objectives, risk management, data governance, lifecycle controls, monitoring, and continual improvement.

Status
Current
Reviewed
2026-08-01

Use boundary: Use for plain-language orientation; consult the standard and qualified assurance professionals for formal implementation decisions.

Open official source

Official-source citations establish provenance and scope. They do not establish LongTermIntelligence.com certification, endorsement, legal advice, client outcomes, or a guarantee that a control is effective.

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