AIGO — ISO/IEC 42001 Evidence Mapping

AIGO — AI Governance Operating Framework

Version: 0.1 Status: Draft Working Name: AIGO Full Name: AI Governance Operating Framework Document Identifier: AIGO-MAP-ISO42001-007 Mapping Standard: ISO/IEC 42001 Mapping Type: Evidence Mapping

1. Purpose

This document defines the relationship between the AIGO evidence model and the evidence expectations associated with an ISO/IEC 42001-aligned AI management system. The purpose of this mapping is to establish a consistent relationship between governance requirements, AI lifecycle activities, risks, controls, decisions, records, monitoring, assurance, management review, and evidence. The mapping provides a foundation for demonstrating that governance requirements have been implemented, operated, monitored, reviewed, and improved.

2. Scope

This document covers evidence associated with:

organizational governance;
AI management-system activities;
AI system registration;
classification;
risk assessment;
risk treatment;
control implementation;
approval;
deployment;
operation;
monitoring;
incidents;
changes;
assurance;
management review;
corrective action;
continual improvement; and
retirement.

The mapping applies to evidence generated throughout the AIGO AI Governance Lifecycle.

3. Evidence Mapping Principle

Evidence is the objective record demonstrating that an AIGO requirement, activity, decision, control, or governance obligation has been performed or achieved. The evidence relationship can be represented as:

Requirement
   ↓
Activity
   ↓
Decision / Control
   ↓
Evidence
   ↓
Verification
   ↓
Assurance
   ↓
Management Review
   ↓
Improvement

Evidence should be sufficiently reliable, attributable, traceable, current, and proportionate to the significance of the activity.

4. Evidence Governance Model

4.1 Evidence Purpose

Evidence should enable the organization to demonstrate:

what was required;
what was performed;
who performed it;
when it was performed;
what decision was made;
what information supported the decision;
what controls were applied;
what outcome resulted; and
whether the activity was reviewed.

4.2 Evidence Architecture

Governance Requirement
       ↓
Process / Procedure
       ↓
Activity
       ↓
Responsible Role
       ↓
Record
       ↓
Evidence Repository
       ↓
Review / Assurance

4.3 Evidence Principle

Evidence should be generated as part of normal governance and operational processes rather than created retrospectively solely for an assessment.

5. Evidence Categories

5.1 Governance Evidence

Governance evidence may include:

policies;
governance charters;
role definitions;
committee records;
governance decisions;
approvals;
risk acceptance records;
exceptions; and
management reviews.

5.2 Lifecycle Evidence

Lifecycle evidence may include:

system registration;
classification;
requirements;
assessments;
approvals;
deployment records;
monitoring records;
change records; and
retirement records.

5.3 Risk Evidence

Risk evidence may include:

risk assessments;
risk registers;
risk treatment plans;
residual-risk evaluations;
acceptance decisions;
risk monitoring; and
risk reviews.

5.4 Control Evidence

Control evidence may include:

control assessments;
control implementation records;
testing results;
control-owner attestations;
monitoring outputs;
exceptions; and
corrective actions.

6. Evidence Lifecycle

6.1 Evidence Lifecycle Model

Evidence should be managed throughout its complete lifecycle.

Evidence Requirement
       ↓
Evidence Generation
       ↓
Evidence Capture
       ↓
Evidence Validation
       ↓
Evidence Storage
       ↓
Evidence Use
       ↓
Evidence Review
       ↓
Retention / Disposal

6.2 Evidence Lifecycle Requirements

Evidence management should address:

creation;
identification;
ownership;
classification;
validation;
storage;
access;
retention;
review; and
disposal.

7. Evidence Ownership

7.1 Purpose

Evidence ownership establishes responsibility for ensuring that required evidence exists and remains reliable.

7.2 Evidence Owner

An evidence owner should be responsible for:

identifying required evidence;
ensuring evidence is generated;
ensuring evidence is accurate;
maintaining evidence availability;
responding to evidence requests; and
coordinating remediation where evidence is incomplete.

7.3 Ownership Model

Requirement
   ↓
Evidence Requirement
   ↓
Evidence Owner
   ↓
Evidence Creation
   ↓
Evidence Validation
   ↓
Evidence Repository

7.4 Shared Evidence

Where evidence supports multiple requirements, ownership should remain clearly assigned even when multiple functions contribute to the evidence.

8. Evidence Requirements

8.1 Purpose

Evidence requirements define what records are needed to demonstrate implementation and operation.

8.2 Evidence Requirement Definition

A requirement may specify:

evidence type;
source;
owner;
frequency;
retention;
quality criteria;
access restrictions; and
review requirements.

8.3 Evidence Requirement Flow

Governance Requirement
       ↓
Evidence Objective
       ↓
Evidence Type
       ↓
Evidence Owner
       ↓
Generation Method
       ↓
Retention Requirement

9. Evidence Traceability

9.1 Purpose

Evidence traceability connects evidence to the requirement, activity, control, system, and decision that generated it.

9.2 Traceability Model

Requirement
   ↓
AI System
   ↓
Lifecycle Stage
   ↓
Risk
   ↓
Control
   ↓
Evidence
   ↓
Decision
   ↓
Accountable Role

9.3 Traceability Identifier

Material evidence should, where practical, be traceable using identifiers such as:

AI system ID;
requirement ID;
risk ID;
control ID;
decision ID;
evidence ID;
incident ID; or
change ID.

10. Evidence Quality

10.1 Purpose

Evidence quality determines whether evidence is sufficiently reliable to support governance decisions and assurance conclusions.

10.2 Quality Characteristics

Evidence should be assessed for:

authenticity;
accuracy;
completeness;
relevance;
timeliness;
consistency;
traceability;
integrity; and
accessibility.

10.3 Evidence Quality Model

Evidence
   ↓
Authenticity
   ↓
Accuracy
   ↓
Completeness
   ↓
Relevance
   ↓
Timeliness
   ↓
Traceability
   ↓
Evidence Quality

10.4 Quality Principle

Evidence quality should be proportionate to the significance and risk of the associated requirement or decision.

11. Evidence Completeness

11.1 Purpose

Evidence completeness determines whether the available evidence adequately covers the applicable requirement.

11.2 Completeness Factors

Assessment may consider:

required records;
responsible roles;
applicable lifecycle stages;
applicable controls;
decision records;
monitoring results;
review records; and
corrective actions.

11.3 Completeness Model

Requirement
   ↓
Required Evidence Set
   ↓
Available Evidence
   ↓
Gap Analysis
   ↓
Evidence Gap
   ↓
Remediation

12. Evidence Authenticity

12.1 Purpose

Evidence authenticity establishes confidence that a record is what it claims to be and originated from the stated source.

12.2 Authenticity Factors

Controls may include:

source identification;
system-generated records;
access controls;
timestamps;
approvals;
digital signatures;
version history; and
controlled repositories.

12.3 Authenticity Relationship

Source
   ↓
Record Creation
   ↓
Identity / Attribution
   ↓
Integrity Protection
   ↓
Controlled Storage
   ↓
Authentic Evidence

13. Evidence Integrity

13.1 Purpose

Evidence integrity ensures that records remain protected against unauthorized modification or destruction.

13.2 Integrity Controls

Measures may include:

access control;
version control;
immutable storage;
audit logs;
backups;
change tracking; and
segregation of duties.

13.3 Integrity Model

Evidence Creation
       ↓
Integrity Control
       ↓
Controlled Storage
       ↓
Access Monitoring
       ↓
Integrity Verification

14. Evidence Availability

14.1 Purpose

Required evidence should remain accessible to authorized persons for the required period.

14.2 Availability Requirements

Evidence management should consider:

repository availability;
access permissions;
backup;
recovery;
retention;
searchability; and
business continuity.

14.3 Availability Model

Evidence
   ↓
Storage
   ↓
Backup
   ↓
Recovery
   ↓
Authorized Access
   ↓
Evidence Availability

15. Evidence Retention

15.1 Purpose

Evidence should be retained for periods appropriate to legal, regulatory, contractual, operational, governance, and assurance requirements.

15.2 Retention Factors

Retention decisions may consider:

legal obligations;
regulatory obligations;
contractual obligations;
AI system lifecycle;
risk;
audit requirements;
incident investigation;
management review; and
organizational policy.

15.3 Retention Lifecycle

Evidence Created
       ↓
Active Use
       ↓
Retention Period
       ↓
Review
       ↓
Archive / Dispose

16. Evidence Classification

16.1 Purpose

Evidence may require classification according to sensitivity, confidentiality, integrity, criticality, or other organizational requirements.

16.2 Classification Factors

Classification may consider:

personal data;
confidential information;
security-sensitive information;
proprietary information;
commercially sensitive information;
regulatory information; and
public information.

16.3 Classification Model

Evidence
   ↓
Sensitivity Assessment
   ↓
Classification
   ↓
Access Rules
   ↓
Handling Requirements
   ↓
Retention / Disposal

17. Evidence Access

17.1 Purpose

Access to evidence should be restricted to authorized persons and purposes.

17.2 Access Principles

Access should follow:

least privilege;
role-based access;
need-to-know;
segregation of duties; and
appropriate authentication.

17.3 Access Model

Evidence
   ↓
Classification
   ↓
Authorized Role
   ↓
Access Decision
   ↓
Access
   ↓
Access Record

18. Evidence and AI System Registration

18.1 Purpose

AI system registration provides foundational evidence that an AI system has entered the organization’s governance framework.

18.2 Registration Evidence

Evidence may include:

registration record;
system identifier;
owner;
purpose;
intended use;
lifecycle stage;
classification;
dependencies; and
approval status.

18.3 Registration Evidence Flow

AI System
   ↓
Registration
   ↓
System Profile
   ↓
Governance Record
   ↓
Evidence Repository

19. Evidence and AI Classification

19.1 Purpose

Classification evidence demonstrates how an AI system has been categorized according to applicable organizational or external requirements.

19.2 Classification Evidence

Evidence may include:

classification criteria;
completed assessment;
classification decision;
decision authority;
rationale;
date; and
review record.

19.3 Classification Traceability

Classification Criteria
       ↓
AI System Assessment
       ↓
Classification Decision
       ↓
Rationale
       ↓
Approval
       ↓
Evidence

20. Evidence and Risk Assessment

20.1 Purpose

Risk evidence demonstrates that AI risks have been identified, analyzed, evaluated, and treated.

20.2 Risk Evidence

Evidence may include:

risk assessment;
risk register;
impact analysis;
likelihood assessment;
risk rating;
treatment decision;
residual risk; and
acceptance record.

20.3 Risk Evidence Flow

AI System
   ↓
Risk Identification
   ↓
Risk Analysis
   ↓
Risk Evaluation
   ↓
Treatment
   ↓
Residual Risk
   ↓
Acceptance / Escalation
   ↓
Evidence

21. Evidence and Risk Treatment

21.1 Purpose

Risk-treatment evidence demonstrates that identified risks have been addressed according to approved requirements.

21.2 Treatment Evidence

Evidence may include:

treatment plan;
selected controls;
implementation records;
residual-risk assessment;
responsible owner;
completion evidence; and
effectiveness assessment.

21.3 Treatment Traceability

Risk
   ↓
Treatment Decision
   ↓
Control
   ↓
Implementation
   ↓
Evidence
   ↓
Effectiveness

22. Evidence and Control Assessment

22.1 Purpose

Control-assessment evidence demonstrates whether applicable controls have been implemented and are operating effectively.

22.2 Assessment Evidence

Evidence may include:

control assessment;
test procedure;
test result;
supporting record;
control-owner confirmation;
deficiency;
corrective action; and
reassessment.

22.3 Control Assessment Flow

Control Requirement
       ↓
Control Implementation
       ↓
Assessment
       ↓
Evidence
       ↓
Effectiveness Result
       ↓
Corrective Action

23. Evidence and Approval

23.1 Purpose

Approval evidence demonstrates that an authorized person or body approved a required AI governance decision.

23.2 Approval Evidence

Approval records may contain:

subject;
decision;
authority;
date;
conditions;
supporting evidence;
approver; and
review requirements.

23.3 Approval Flow

Assessment
   ↓
Evidence Package
   ↓
Authorized Review
   ↓
Approval Decision
   ↓
Approval Record
   ↓
Implementation

24. Evidence and Deployment

24.1 Purpose

Deployment evidence demonstrates that an AI system was deployed under an approved governance state.

24.2 Deployment Evidence

Evidence may include:

deployment approval;
release record;
configuration;
validation;
security checks;
monitoring configuration;
responsible owner; and
deployment date.

24.3 Deployment Flow

Approved AI System
       ↓
Deployment Preparation
       ↓
Validation
       ↓
Deployment
       ↓
Verification
       ↓
Operational Evidence

25. Evidence and Operation

25.1 Purpose

Operational evidence demonstrates that the AI system is operating according to defined requirements.

25.2 Operational Evidence

Evidence may include:

operational logs;
system performance;
access records;
human oversight records;
control checks;
incidents;
exceptions; and
operational reviews.

25.3 Operational Evidence Flow

AI System
   ↓
Operation
   ↓
Operational Data
   ↓
Monitoring
   ↓
Review
   ↓
Evidence

26. Evidence and Monitoring

26.1 Purpose

Monitoring evidence demonstrates that relevant AI system, risk, control, and governance indicators are being observed.

26.2 Monitoring Evidence

Evidence may include:

monitoring reports;
dashboards;
alerts;
performance records;
risk indicators;
control indicators;
threshold breaches; and
management responses.

26.3 Monitoring Evidence Flow

Monitoring Requirement
       ↓
Indicator
       ↓
Measurement
       ↓
Result
       ↓
Analysis
       ↓
Decision / Action
       ↓
Evidence

27. Evidence and Incident Management

27.1 Purpose

Incident evidence supports investigation, response, accountability, corrective action, and lessons learned.

27.2 Incident Evidence

Evidence may include:

incident report;
detection record;
timeline;
investigation;
impact assessment;
response actions;
communications;
root-cause analysis; and
corrective action.

27.3 Incident Evidence Flow

Incident
   ↓
Detection
   ↓
Recording
   ↓
Investigation
   ↓
Response
   ↓
Corrective Action
   ↓
Closure
   ↓
Lessons Learned

28. Evidence and Change Management

28.1 Purpose

Change evidence demonstrates that material changes were appropriately assessed, approved, implemented, and reviewed.

28.2 Change Evidence

Evidence may include:

change request;
impact assessment;
risk assessment;
approval;
implementation record;
validation;
post-change review; and
updated documentation.

28.3 Change Evidence Flow

Change Request
      ↓
Impact Assessment
      ↓
Risk Assessment
      ↓
Approval
      ↓
Implementation
      ↓
Validation
      ↓
Post-Change Review
      ↓
Evidence

29. Evidence and Assurance

29.1 Purpose

Evidence provides the foundation for assurance activities.

29.2 Assurance Evidence

Assurance may use:

governance records;
risk records;
control evidence;
monitoring evidence;
operational records;
incident records;
change records; and
management-review records.

29.3 Assurance Flow

Evidence Set
   ↓
Evidence Review
   ↓
Testing / Verification
   ↓
Assurance Finding
   ↓
Conclusion
   ↓
Corrective Action

30. Evidence and Management Review

30.1 Purpose

Management review should be supported by relevant evidence regarding AI governance performance and the effectiveness of the AI management system.

30.2 Management Review Evidence

Evidence may include:

performance information;
risk information;
monitoring results;
incidents;
assurance findings;
stakeholder feedback;
compliance information;
corrective actions; and
improvement opportunities.

30.3 Management Review Evidence Flow

Evidence
   ↓
Management Information
   ↓
Management Review
   ↓
Decision
   ↓
Action
   ↓
Recorded Outcome

31. Evidence and Corrective Action

31.1 Purpose

Corrective-action evidence demonstrates that identified deficiencies have been addressed.

31.2 Corrective Action Evidence

Evidence may include:

finding;
root-cause analysis;
corrective-action plan;
assigned owner;
implementation evidence;
effectiveness assessment; and
closure approval.

31.3 Corrective Action Flow

Finding
   ↓
Root Cause
   ↓
Corrective Action
   ↓
Implementation
   ↓
Evidence
   ↓
Effectiveness Review
   ↓
Closure

32. Evidence and Continual Improvement

32.1 Purpose

Improvement evidence demonstrates that lessons learned and performance information are converted into governance or operational improvements.

32.2 Improvement Evidence

Evidence may include:

improvement proposal;
decision;
change request;
revised control;
revised procedure;
implementation record;
effectiveness assessment; and
management-review record.

32.3 Improvement Flow

Performance / Finding
       ↓
Improvement Opportunity
       ↓
Decision
       ↓
Change
       ↓
Implementation
       ↓
Verification
       ↓
Evidence

33. Evidence and Retirement

33.1 Purpose

Retirement evidence demonstrates that an AI system has been appropriately decommissioned and that relevant records and obligations have been addressed.

33.2 Retirement Evidence

Evidence may include:

retirement decision;
approval;
risk assessment;
dependency assessment;
data disposition;
access removal;
decommissioning record;
verification; and
closure record.

33.3 Retirement Evidence Flow

Retirement Decision
       ↓
Planning
       ↓
Decommissioning
       ↓
Verification
       ↓
Data / Record Disposition
       ↓
Closure
       ↓
Evidence

34. Evidence Repository

34.1 Purpose

A controlled repository should provide an appropriate location for governance and AI lifecycle evidence.

34.2 Repository Requirements

The repository should support, as appropriate:

controlled access;
search;
metadata;
version control;
auditability;
retention;
backup;
retrieval; and
secure disposal.

34.3 Repository Model

Evidence
   ↓
Classification
   ↓
Metadata
   ↓
Controlled Repository
   ↓
Access / Retrieval
   ↓
Review / Assurance

35. Evidence Metadata

35.1 Purpose

Metadata improves evidence identification, retrieval, classification, and traceability.

35.2 Recommended Metadata

Evidence records may include:

evidence ID;
title;
source;
owner;
AI system ID;
lifecycle stage;
requirement ID;
risk ID;
control ID;
creation date;
effective date;
review date;
classification;
retention period; and
status.

35.3 Metadata Model

Evidence
   ↓
Identity
   ↓
Context
   ↓
Ownership
   ↓
Classification
   ↓
Lifecycle Information
   ↓
Retention Information

36. Evidence Version Control

36.1 Purpose

Version control ensures that the organization can identify the applicable version of a governance record or evidence artifact.

36.2 Version-Control Requirements

Where applicable, records should maintain:

version;
revision date;
author;
approver;
change description;
effective date; and
superseded version.

36.3 Version Flow

Evidence Version 1
       ↓
Change
       ↓
Review
       ↓
Approval
       ↓
Evidence Version 2
       ↓
Superseded Version Retained

37. Evidence Review

37.1 Purpose

Evidence should be periodically reviewed to ensure that it remains accurate, relevant, complete, and accessible.

37.2 Review Triggers

Review may be triggered by:

scheduled review;
material change;
incident;
audit;
assurance;
regulatory change;
control failure; or
management review.

37.3 Review Flow

Evidence
   ↓
Review Trigger
   ↓
Evidence Review
   ↓
Valid?
 ↙       ↘
Yes       No
 ↓         ↓
Retain   Update / Replace

38. Evidence Gaps

38.1 Purpose

Evidence gaps identify situations where required evidence is missing, incomplete, unreliable, or inaccessible.

38.2 Evidence Gap Categories

Gaps may include:

missing evidence;
incomplete evidence;
outdated evidence;
inconsistent evidence;
inaccessible evidence;
unverifiable evidence; or
insufficient evidence.

38.3 Gap Management

Requirement
   ↓
Evidence Expected
   ↓
Evidence Available
   ↓
Gap Identified
   ↓
Impact Assessment
   ↓
Remediation
   ↓
Verification

39. Evidence Deficiencies

39.1 Purpose

Evidence deficiencies should be evaluated according to their potential effect on governance, risk, control effectiveness, compliance, and assurance conclusions.

39.2 Deficiency Evaluation

The organization may consider:

severity;
scope;
recurrence;
affected systems;
affected controls;
associated risk; and
management impact.

39.3 Deficiency Flow

Evidence Deficiency
       ↓
Assessment
       ↓
Risk / Impact
       ↓
Corrective Action
       ↓
Verification
       ↓
Closure

40. Evidence and ISO/IEC 42001 Traceability

40.1 Purpose

The evidence mapping establishes a relationship between relevant ISO/IEC 42001 requirements and AIGO evidence artifacts.

40.2 Traceability Model

ISO/IEC 42001 Requirement
          ↓
AIGO Governance Requirement
          ↓
AIGO Process / Control
          ↓
Required Evidence
          ↓
Evidence Record
          ↓
Verification / Assurance

40.3 Mapping Principle

The existence of an evidence artifact does not automatically demonstrate conformity. Evidence should demonstrate that the applicable requirement has been implemented and operated effectively within the organization’s context.

41. Evidence Package Model

41.1 Purpose

For significant governance decisions or assurance activities, evidence may be assembled into a controlled evidence package.

41.2 Evidence Package Components

An evidence package may contain:

scope;
requirement;
AI system;
risk assessment;
controls;
supporting evidence;
decision;
approval;
monitoring results;
assurance results; and
conclusion.

41.3 Evidence Package Flow

Requirement
   ↓
Evidence Collection
   ↓
Evidence Validation
   ↓
Evidence Package
   ↓
Review
   ↓
Decision / Assurance

42. Evidence Sufficiency

42.1 Purpose

Evidence sufficiency determines whether available evidence is adequate for the intended governance or assurance purpose.

42.2 Sufficiency Factors

Assessment may consider:

completeness;
reliability;
relevance;
independence;
timeliness;
traceability;
consistency; and
risk significance.

42.3 Sufficiency Model

Evidence
   ↓
Quality Assessment
   ↓
Completeness Assessment
   ↓
Relevance Assessment
   ↓
Risk Context
   ↓
Sufficient?
 ↙        ↘
Yes        No
 ↓          ↓
Use       Remediate

43. Evidence for Governance Decisions

43.1 Purpose

Governance decisions should be supported by evidence proportionate to the significance of the decision.

43.2 Decision Evidence

Examples include evidence supporting:

system approval;
risk acceptance;
material change;
exception;
deployment;
suspension;
continuation;
retirement; and
corrective action.

43.3 Decision Evidence Model

Decision Requirement
       ↓
Supporting Information
       ↓
Risk / Impact
       ↓
Evidence
       ↓
Authorized Decision
       ↓
Decision Record

44. Evidence for High-Risk AI Systems

44.1 Purpose

Higher-risk AI systems may require enhanced evidence because the consequences of governance failure may be greater.

44.2 Enhanced Evidence

Enhanced evidence may include:

detailed risk assessment;
impact assessment;
stronger approval evidence;
control-testing evidence;
human-oversight records;
monitoring results;
assurance results; and
management-review records.

44.3 Proportionality Principle

Evidence requirements should increase where risk, impact, complexity, autonomy, or regulatory significance increases.

45. Evidence and Third Parties

45.1 Purpose

Third-party AI systems and services may require evidence demonstrating that relevant supplier governance requirements have been addressed.

45.2 Third-Party Evidence

Evidence may include:

supplier assessment;
contractual requirements;
supplier attestations;
assurance reports;
security documentation;
privacy documentation;
service performance;
incidents; and
change notifications.

45.3 Third-Party Evidence Flow

Third-Party AI Service
       ↓
Supplier Assessment
       ↓
Contractual Requirements
       ↓
Evidence
       ↓
Monitoring
       ↓
Assurance

46. Evidence and External Obligations

46.1 Purpose

Evidence should support applicable legal, regulatory, contractual, and organizational obligations.

46.2 Obligation Traceability

External Obligation
       ↓
Internal Requirement
       ↓
Control / Procedure
       ↓
Evidence
       ↓
Verification
       ↓
Compliance / Assurance

46.3 Obligation Principle

The organization should identify applicable obligations and determine what evidence is required to demonstrate implementation and ongoing compliance.

47. Evidence and Stakeholder Requirements

47.1 Purpose

Stakeholder requirements may generate evidence obligations.

47.2 Stakeholder Evidence

Relevant evidence may include:

communications;
disclosures;
approvals;
complaints;
feedback;
stakeholder decisions;
consultations; and
response records.

47.3 Stakeholder Flow

Stakeholder Requirement
       ↓
Governance Requirement
       ↓
Action
       ↓
Evidence
       ↓
Stakeholder Communication
       ↓
Review

48. Evidence and Organizational Context

48.1 Purpose

Evidence should remain aligned with the organization’s current internal and external context.

48.2 Context Changes

Evidence requirements may need review when there are changes to:

organizational strategy;
legal environment;
regulatory environment;
technology;
AI portfolio;
stakeholders;
risk profile; or
organizational structure.

48.3 Context Review

Organizational Context
       ↓
Context Change
       ↓
Governance Review
       ↓
Evidence Requirement Review
       ↓
Updated Evidence Model

49. Evidence and Performance Indicators

49.1 Purpose

Performance indicators provide evidence regarding the effectiveness and performance of AI governance.

49.2 Example Indicators

Indicators may include:

percentage of registered AI systems;
percentage of completed risk assessments;
control assessment completion;
monitoring coverage;
overdue corrective actions;
incident frequency;
assurance finding closure;
training completion; and
management-review action completion.

49.3 Indicator Evidence Flow

Objective
   ↓
Indicator
   ↓
Measurement
   ↓
Evidence
   ↓
Analysis
   ↓
Management Decision

50. Evidence and Management Information

50.1 Purpose

Evidence should be transformed into useful management information for governance decision-making.

50.2 Management Information

Management information may summarize:

AI portfolio;
risk;
control performance;
incidents;
monitoring;
assurance;
compliance;
changes; and
improvement.

50.3 Information Flow

Evidence
   ↓
Aggregation
   ↓
Analysis
   ↓
Management Information
   ↓
Management Review
   ↓
Decision

51. Evidence and Governance Reporting

51.1 Purpose

Governance reporting communicates relevant evidence and conclusions to authorized decision-makers.

51.2 Reporting Content

Reports may include:

status;
risks;
issues;
control effectiveness;
incidents;
performance;
assurance findings;
actions; and
decisions required.

51.3 Reporting Flow

Evidence
   ↓
Analysis
   ↓
Governance Report
   ↓
Decision Authority
   ↓
Decision
   ↓
Action

52. Evidence and Auditability

52.1 Purpose

Auditability enables an authorized reviewer to reconstruct relevant governance activities and decisions.

52.2 Audit Trail

An audit trail should, where appropriate, connect:

requirement;
activity;
actor;
timestamp;
decision;
evidence;
change; and
outcome.

52.3 Auditability Model

Requirement
   ↓
Activity
   ↓
Actor
   ↓
Timestamp
   ↓
Decision
   ↓
Evidence
   ↓
Outcome

53. Evidence and Segregation of Duties

53.1 Purpose

Where appropriate, evidence should demonstrate separation between incompatible responsibilities.

53.2 Examples

Segregation may be relevant between:

system development and approval;
control implementation and independent assessment;
risk treatment and risk acceptance;
operation and assurance; and
evidence creation and independent verification.

53.3 Segregation Model

Activity
   ↓
Responsible Role
   ↓
Independent Review
   ↓
Verification
   ↓
Evidence

54. Evidence and Independent Assurance

54.1 Purpose

Independent assurance may provide additional confidence where the significance or risk of the activity warrants it.

54.2 Independence Factors

Independence may be assessed based on:

organizational reporting;
conflicts of interest;
technical independence;
decision independence; and
scope of authority.

54.3 Assurance Flow

Evidence
   ↓
Independent Review
   ↓
Testing
   ↓
Finding / Conclusion
   ↓
Management Response

55. Evidence and Corrective-Action Effectiveness

55.1 Purpose

Evidence should demonstrate whether corrective actions actually addressed the underlying deficiency.

55.2 Effectiveness Evidence

Evidence may demonstrate:

action completion;
root cause addressed;
control improvement;
recurrence reduction;
risk reduction;
validation; and
closure approval.

55.3 Effectiveness Flow

Deficiency
   ↓
Corrective Action
   ↓
Implementation Evidence
   ↓
Effectiveness Test
   ↓
Result
   ↓
Closure / Further Action

56. Evidence and Lessons Learned

56.1 Purpose

Lessons learned provide evidence that experience is converted into organizational improvement.

56.2 Sources

Lessons may arise from:

incidents;
near misses;
assurance;
audits;
management reviews;
changes;
stakeholder feedback; and
operational experience.

56.3 Lessons-Learned Flow

Experience
   ↓
Analysis
   ↓
Lesson Identified
   ↓
Improvement Decision
   ↓
Implementation
   ↓
Evidence

57. Evidence and Document Control

57.1 Purpose

Controlled documents are a key component of the AIGO evidence environment.

57.2 Document-Control Evidence

Records may demonstrate:

document owner;
version;
approval;
effective date;
review date;
change history; and
status.

57.3 Document-Control Flow

Document
   ↓
Review
   ↓
Approval
   ↓
Controlled Release
   ↓
Use
   ↓
Review / Revision

58. Evidence and Records Management

58.1 Purpose

Records management ensures that governance evidence remains identifiable, protected, retrievable, and appropriately retained.

58.2 Records Requirements

Records management should address:

identification;
ownership;
storage;
access;
retention;
retrieval;
protection; and
disposal.

58.3 Records Flow

Record
   ↓
Classification
   ↓
Storage
   ↓
Retention
   ↓
Retrieval
   ↓
Review
   ↓
Disposition

59. Evidence Mapping Matrix

59.1 Conceptual Matrix

AIGO Governance Area	Evidence Examples	Primary Evidence Purpose
Governance	Policies, decisions, committee records	Demonstrate governance direction
Roles	Role descriptions, responsibility assignments	Demonstrate accountability
Registration	AI system records	Demonstrate governance entry
Classification	Classification assessments	Demonstrate categorization
Risk	Risk assessments and registers	Demonstrate risk management
Controls	Control assessments and test results	Demonstrate control implementation
Approval	Approval records	Demonstrate authorized decisions
Deployment	Release and validation records	Demonstrate controlled deployment
Operation	Operational records and logs	Demonstrate ongoing operation
Monitoring	Monitoring reports and indicators	Demonstrate oversight
Incidents	Incident records	Demonstrate response and learning
Changes	Change records	Demonstrate controlled change
Assurance	Assurance reports	Demonstrate independent evaluation
Management Review	Review records	Demonstrate management oversight
Improvement	Corrective-action and improvement records	Demonstrate continual improvement
Retirement	Retirement records	Demonstrate controlled closure

60. Evidence Traceability Matrix Structure

60.1 Recommended Structure

A detailed implementation matrix may contain:

Field	Description
Requirement ID	Identifier of the applicable requirement
AIGO Reference	Relevant AIGO framework reference
AI System ID	Applicable AI system
Lifecycle Stage	Relevant lifecycle stage
Risk ID	Associated risk
Control ID	Associated control
Evidence ID	Evidence identifier
Evidence Owner	Responsible evidence owner
Evidence Location	Controlled repository reference
Evidence Status	Current evidence status
Review Date	Date of evidence review
Assurance Status	Assurance or verification status

60.2 Traceability Principle

The matrix should support reconstruction of the relationship between governance requirements and evidence without requiring reliance on undocumented institutional knowledge.

61. Evidence Status

61.1 Evidence Status Categories

Evidence may be classified as:

planned;
requested;
generated;
validated;
approved;
current;
expired;
superseded;
deficient; or
retired.

61.2 Status Flow

Planned
   ↓
Requested
   ↓
Generated
   ↓
Validated
   ↓
Current
   ↓
Superseded / Expired
   ↓
Archived / Retired

62. Evidence Exceptions

62.1 Purpose

Evidence exceptions provide a controlled mechanism for addressing situations where expected evidence cannot be produced or maintained.

62.2 Exception Requirements

An evidence exception should document:

missing evidence;
reason;
impact;
associated risk;
compensating evidence or controls;
owner;
approval;
remediation plan; and
review date.

62.3 Exception Flow

Evidence Gap
   ↓
Exception Assessment
   ↓
Risk Evaluation
   ↓
Compensating Measure
   ↓
Approval
   ↓
Remediation
   ↓
Closure

63. Evidence Escalation

63.1 Purpose

Material evidence deficiencies should be escalated to the appropriate governance authority.

63.2 Escalation Triggers

Escalation may be required where:

evidence is materially missing;
evidence integrity is questionable;
evidence indicates control failure;
evidence indicates significant risk;
repeated deficiencies occur; or
assurance conclusions are affected.

63.3 Escalation Flow

Evidence Issue
   ↓
Initial Assessment
   ↓
Material?
 ↙        ↘
No         Yes
 ↓           ↓
Resolve    Escalate
              ↓
      Governance Authority
              ↓
            Decision

64. Evidence and Continual Evidence Improvement

64.1 Purpose

The evidence model should itself be subject to continual improvement.

64.2 Improvement Inputs

Evidence-model improvement may be based on:

assurance findings;
audit findings;
repeated evidence gaps;
operational experience;
changes in requirements;
technology changes; and
stakeholder feedback.

64.3 Improvement Flow

Evidence Experience
       ↓
Gap / Opportunity
       ↓
Analysis
       ↓
Evidence Model Change
       ↓
Implementation
       ↓
Verification

65. Evidence Governance Responsibilities

65.1 Governance Responsibility

AI governance should establish the overall expectations for evidence management.

65.2 Evidence Owner Responsibility

Evidence owners should ensure that required records are generated and maintained.

65.3 Control Owner Responsibility

Control owners should ensure that evidence demonstrates control operation.

65.4 Assurance Responsibility

Assurance functions should evaluate evidence sufficiency and reliability within the defined scope of their work.

65.5 Management Responsibility

Management should use relevant evidence to support governance decisions and management review.

66. Evidence and Proportionality

66.1 Purpose

Evidence requirements should be proportionate to the nature and significance of the AI system and associated risks.

66.2 Proportionality Factors

Factors may include:

risk;
impact;
autonomy;
scale;
complexity;
affected stakeholders;
regulatory significance;
system criticality; and
organizational context.

66.3 Proportionality Model

AI System Characteristics
       ↓
Risk / Impact
       ↓
Governance Requirements
       ↓
Evidence Requirements
       ↓
Evidence Depth

67. Evidence and AI Governance Maturity

67.1 Purpose

Evidence maturity indicates the organization’s ability to consistently produce and manage reliable governance evidence.

67.2 Maturity Characteristics

Evidence maturity may progress from:

informal;
repeatable;
defined;
managed; to
optimized.

67.3 Maturity Relationship

Evidence Capture
       ↓
Consistency
       ↓
Traceability
       ↓
Measurement
       ↓
Assurance
       ↓
Optimization

68. Evidence and ISO/IEC 42001 Management-System Relationship

68.1 Purpose

Evidence supports demonstration that the AI management system is established, implemented, maintained, and continually improved.

68.2 Relationship

ISO/IEC 42001 Management-System Requirement
              ↓
AIGO Governance Requirement
              ↓
AIGO Process
              ↓
AIGO Control
              ↓
Evidence
              ↓
Monitoring / Assurance
              ↓
Management Review

68.3 Evidence Principle

Evidence should demonstrate not only that documentation exists, but that relevant processes and controls are implemented and operating.

69. Evidence and Management-System Effectiveness

69.1 Purpose

Evidence should support evaluation of whether the AI management system achieves intended outcomes.

69.2 Effectiveness Evidence

Evidence may include:

performance indicators;
risk outcomes;
control effectiveness;
incidents;
assurance results;
corrective actions; and
management decisions.

69.3 Effectiveness Model

Requirement
   ↓
Implementation
   ↓
Operation
   ↓
Outcome
   ↓
Measurement
   ↓
Evaluation
   ↓
Improvement

70. Evidence Review Frequency

70.1 Purpose

Evidence should be reviewed at intervals appropriate to its significance and lifecycle.

70.2 Scheduled Review

Scheduled review may be based on:

organizational policy;
risk;
control criticality;
evidence sensitivity;
lifecycle stage;
regulatory requirements; and
management requirements.

70.3 Triggered Review

Review may also be triggered by:

incidents;
material changes;
audit findings;
assurance findings;
regulatory changes;
risk changes; or
evidence deficiencies.

70.4 Review Flow

Evidence
   ↓
Scheduled / Triggered Review
   ↓
Validity Assessment
   ↓
Update / Retain / Replace
   ↓
Verification

71. Evidence Control Effectiveness

71.1 Purpose

Evidence controls should themselves be assessed to ensure that evidence remains trustworthy.

71.2 Control Areas

Controls may address:

access;
modification;
deletion;
versioning;
backup;
retention;
retrieval; and
audit logging.

71.3 Effectiveness Model

Evidence Control
       ↓
Implementation
       ↓
Operation
       ↓
Evidence
       ↓
Control Assessment
       ↓
Effectiveness Decision

72. Evidence Retrieval

72.1 Purpose

Evidence should be retrievable within a reasonable period for authorized governance, assurance, audit, regulatory, or operational purposes.

72.2 Retrieval Requirements

Retrieval should support:

evidence identification;
search;
filtering;
authorization;
integrity verification; and
audit trail.

72.3 Retrieval Flow

Evidence Request
       ↓
Authorization
       ↓
Evidence Search
       ↓
Retrieval
       ↓
Integrity Verification
       ↓
Delivery / Review

73. Evidence Disposal

73.1 Purpose

Evidence should be disposed of in accordance with applicable retention requirements and authorized disposal processes.

73.2 Disposal Requirements

Disposal should consider:

retention expiry;
legal holds;
regulatory requirements;
contractual requirements;
security;
privacy;
business requirements; and
authorization.

73.3 Disposal Flow

Retention Expiry
       ↓
Disposition Review
       ↓
Legal / Regulatory Check
       ↓
Authorization
       ↓
Secure Disposal / Archive
       ↓
Disposal Record

74. Evidence Mapping Control Model

74.1 Control Objective

The evidence mapping process should ensure that material AIGO and ISO/IEC 42001 requirements can be connected to objective records.

74.2 Control Relationship

Requirement
   ↓
Control Objective
   ↓
Control
   ↓
Evidence Requirement
   ↓
Evidence
   ↓
Verification
   ↓
Assurance

75. Evidence Mapping Governance Cycle

75.1 Governance Cycle

The evidence mapping should be maintained as part of the governance lifecycle.

Define Requirement
       ↓
Identify Evidence
       ↓
Assign Owner
       ↓
Generate Evidence
       ↓
Validate Evidence
       ↓
Review Evidence
       ↓
Assure Evidence
       ↓
Improve Evidence Model

75.2 Living Document Principle

This mapping should be treated as a controlled and living document. It should be updated when:

AIGO requirements change;
ISO/IEC 42001 requirements or interpretations change;
organizational context changes;
AI systems materially change;
governance processes change;
controls change;
evidence requirements change; or
assurance identifies a mapping deficiency.

76. Final Evidence Traceability Model

76.1 End-to-End Model

Organization
      ↓
Governance
      ↓
Requirement
      ↓
AI System
      ↓
Lifecycle Stage
      ↓
Risk
      ↓
Control
      ↓
Activity
      ↓
Decision
      ↓
Evidence
      ↓
Monitoring
      ↓
Assurance
      ↓
Management Review
      ↓
Improvement

76.2 Traceability Principle

The complete evidence chain should enable an authorized reviewer to understand how an organizational requirement was translated into an operational activity and how that activity produced evidence supporting governance, assurance, and continual improvement.

77. Document Status

Document: AIGO — ISO/IEC 42001 Evidence Mapping Version: 0.1 Status: Draft Working Name: AIGO Full Name: AI Governance Operating Framework Document Identifier: AIGO-MAP-ISO42001-007 Document Type: Evidence Mapping This document establishes the evidence-level relationship between ISO/IEC 42001 and the AIGO AI Governance Operating Framework and provides the foundation for evidence generation, traceability, validation, retention, monitoring, assurance, management review, and continual improvement.

78. End of Mapping Document

78.1 Final Status

AIGO — ISO/IEC 42001 Evidence Mapping Document ID: AIGO-MAP-ISO42001-007 Version: 0.1 Status: Draft End of Document

​AIGO — ISO/IEC 42001 Evidence Mapping

​AIGO — AI Governance Operating Framework

​1. Purpose

​2. Scope

​3. Evidence Mapping Principle

​4. Evidence Governance Model

​4.1 Evidence Purpose

​4.2 Evidence Architecture

​4.3 Evidence Principle

​5. Evidence Categories

​5.1 Governance Evidence

​5.2 Lifecycle Evidence

​5.3 Risk Evidence

​5.4 Control Evidence

​6. Evidence Lifecycle

​6.1 Evidence Lifecycle Model

​6.2 Evidence Lifecycle Requirements

​7. Evidence Ownership

​7.1 Purpose

​7.2 Evidence Owner

​7.3 Ownership Model

​7.4 Shared Evidence

​8. Evidence Requirements

​8.1 Purpose

​8.2 Evidence Requirement Definition

​8.3 Evidence Requirement Flow

​9. Evidence Traceability

​9.1 Purpose

​9.2 Traceability Model

​9.3 Traceability Identifier

​10. Evidence Quality

​10.1 Purpose

​10.2 Quality Characteristics

​10.3 Evidence Quality Model

​10.4 Quality Principle

​11. Evidence Completeness

​11.1 Purpose

​11.2 Completeness Factors

​11.3 Completeness Model

​12. Evidence Authenticity

​12.1 Purpose

​12.2 Authenticity Factors

​12.3 Authenticity Relationship

​13. Evidence Integrity

​13.1 Purpose

​13.2 Integrity Controls

​13.3 Integrity Model

​14. Evidence Availability

​14.1 Purpose

​14.2 Availability Requirements

​14.3 Availability Model

​15. Evidence Retention

​15.1 Purpose

​15.2 Retention Factors

​15.3 Retention Lifecycle

​16. Evidence Classification

​16.1 Purpose

​16.2 Classification Factors

​16.3 Classification Model

​17. Evidence Access

​17.1 Purpose

​17.2 Access Principles

​17.3 Access Model

​18. Evidence and AI System Registration

​18.1 Purpose

​18.2 Registration Evidence

​18.3 Registration Evidence Flow

​19. Evidence and AI Classification

​19.1 Purpose

​19.2 Classification Evidence

​19.3 Classification Traceability

​20. Evidence and Risk Assessment

​20.1 Purpose

​20.2 Risk Evidence

​20.3 Risk Evidence Flow

​21. Evidence and Risk Treatment

​21.1 Purpose

​21.2 Treatment Evidence

​21.3 Treatment Traceability

​22. Evidence and Control Assessment

AIGO — ISO/IEC 42001 Evidence Mapping

AIGO — AI Governance Operating Framework

1. Purpose

2. Scope

3. Evidence Mapping Principle

4. Evidence Governance Model

4.1 Evidence Purpose

4.2 Evidence Architecture

4.3 Evidence Principle

5. Evidence Categories

5.1 Governance Evidence

5.2 Lifecycle Evidence

5.3 Risk Evidence

5.4 Control Evidence

6. Evidence Lifecycle

6.1 Evidence Lifecycle Model

6.2 Evidence Lifecycle Requirements

7. Evidence Ownership

7.1 Purpose

7.2 Evidence Owner

7.3 Ownership Model

7.4 Shared Evidence

8. Evidence Requirements

8.1 Purpose

8.2 Evidence Requirement Definition

8.3 Evidence Requirement Flow

9. Evidence Traceability

9.1 Purpose

9.2 Traceability Model

9.3 Traceability Identifier

10. Evidence Quality

10.1 Purpose

10.2 Quality Characteristics

10.3 Evidence Quality Model

10.4 Quality Principle

11. Evidence Completeness

11.1 Purpose

11.2 Completeness Factors

11.3 Completeness Model

12. Evidence Authenticity

12.1 Purpose

12.2 Authenticity Factors

12.3 Authenticity Relationship

13. Evidence Integrity

13.1 Purpose

13.2 Integrity Controls

13.3 Integrity Model

14. Evidence Availability

14.1 Purpose

14.2 Availability Requirements

14.3 Availability Model

15. Evidence Retention

15.1 Purpose

15.2 Retention Factors

15.3 Retention Lifecycle

16. Evidence Classification

16.1 Purpose

16.2 Classification Factors

16.3 Classification Model

17. Evidence Access

17.1 Purpose

17.2 Access Principles

17.3 Access Model

18. Evidence and AI System Registration

18.1 Purpose

18.2 Registration Evidence

18.3 Registration Evidence Flow

19. Evidence and AI Classification

19.1 Purpose

19.2 Classification Evidence

19.3 Classification Traceability

20. Evidence and Risk Assessment

20.1 Purpose

20.2 Risk Evidence

20.3 Risk Evidence Flow

21. Evidence and Risk Treatment

21.1 Purpose

21.2 Treatment Evidence

21.3 Treatment Traceability

22. Evidence and Control Assessment