AIGO — ISO/IEC 42001 Implementation Mapping

AIGO — AI Governance Operating Framework

Version: 0.1 Status: Draft Working Name: AIGO Full Name: AI Governance Operating Framework Document Identifier: AIGO-MAP-ISO42001-008 Mapping Standard: ISO/IEC 42001 Mapping Type: Implementation Mapping

1. Purpose

This document defines how the AIGO AI Governance Operating Framework can be implemented in alignment with ISO/IEC 42001. The purpose of this mapping is to connect ISO/IEC 42001 management-system expectations with the practical implementation architecture of AIGO, including governance, roles, lifecycle management, risk management, controls, procedures, evidence, monitoring, assurance, and continual improvement. This document is intended to provide an implementation bridge between the AIGO framework architecture and an operational AI management system.

2. Scope

This implementation mapping covers:

organizational context;
leadership and governance;
AI management-system planning;
AI system governance;
AI risk management;
AI lifecycle management;
control implementation;
operational procedures;
competence and awareness;
documentation;
evidence;
monitoring;
assurance;
management review;
corrective action;
continual improvement; and
implementation maturity.

The mapping is intended to support implementation across the full AIGO governance lifecycle.

3. Implementation Principle

Implementation should translate governance requirements into operational processes, responsibilities, controls, records, evidence, and measurable outcomes.

ISO/IEC 42001 Requirement
        ↓
AIGO Requirement
        ↓
Governance Process
        ↓
Procedure
        ↓
Control
        ↓
Responsible Role
        ↓
Operational Activity
        ↓
Evidence
        ↓
Monitoring
        ↓
Assurance
        ↓
Improvement

Implementation is therefore not limited to producing documentation. It requires the organization to establish and operate an effective governance system.

4. AIGO Implementation Architecture

The AIGO implementation architecture consists of several interconnected layers.

Framework
   ↓
Guidance
   ↓
Procedures
   ↓
Templates
   ↓
Operational Records
   ↓
Evidence
   ↓
Monitoring
   ↓
Assurance
   ↓
Management Review
   ↓
Continual Improvement

The architecture provides a controlled progression from governance requirements to operational execution.

5. ISO/IEC 42001 Implementation Relationship

The implementation relationship can be represented as:

ISO/IEC 42001
       ↓
Management-System Requirements
       ↓
AIGO Governance Architecture
       ↓
AIGO Implementation Guidance
       ↓
AIGO Procedures
       ↓
Operational Implementation
       ↓
Evidence
       ↓
Evaluation
       ↓
Improvement

AIGO provides the operational structure through which ISO/IEC 42001-aligned governance can be implemented.

6. Organizational Context Implementation

6.1 Purpose

The organization should identify internal and external issues relevant to the AI management system.

6.2 Implementation Activities

Implementation may include:

organizational context analysis;
AI portfolio analysis;
regulatory analysis;
stakeholder identification;
strategic objectives;
technology environment analysis;
risk environment analysis; and
governance capability assessment.

6.3 Implementation Flow

Organizational Context
       ↓
AI Governance Context
       ↓
Interested Parties
       ↓
Requirements
       ↓
AI Management-System Scope
       ↓
Governance Objectives

7. Scope Implementation

7.1 Purpose

The organization should define the boundaries and applicability of the AI management system.

7.2 Scope Considerations

The scope may consider:

organizational units;
AI systems;
AI services;
lifecycle stages;
supporting processes;
third parties;
locations;
technologies; and
applicable governance functions.

7.3 Scope Model

Organization
   ↓
AI Portfolio
   ↓
Applicable Processes
   ↓
Supporting Functions
   ↓
External Dependencies
   ↓
AIMS Scope

8. Leadership Implementation

8.1 Purpose

Leadership should establish direction, accountability, resources, and organizational support for AI governance.

8.2 Leadership Activities

Leadership implementation may include:

approving governance policy;
assigning accountability;
establishing governance bodies;
allocating resources;
reviewing performance;
addressing significant risks; and
supporting continual improvement.

8.3 Leadership Flow

Leadership
   ↓
Policy
   ↓
Objectives
   ↓
Responsibilities
   ↓
Resources
   ↓
Governance Oversight
   ↓
Performance Review

9. AI Governance Policy Implementation

9.1 Purpose

The AI governance policy establishes organizational direction for responsible and controlled use of AI.

9.2 Implementation Requirements

The policy should be:

approved;
communicated;
available to relevant personnel;
reviewed;
maintained; and
aligned with organizational objectives.

9.3 Policy Implementation Flow

Governance Intent
       ↓
Policy Development
       ↓
Review
       ↓
Approval
       ↓
Communication
       ↓
Implementation
       ↓
Review

10. Governance Roles Implementation

10.1 Purpose

Roles and responsibilities should be clearly assigned.

10.2 Role Categories

AIGO may assign responsibilities to:

governing bodies;
executive management;
AI governance functions;
system owners;
risk owners;
control owners;
technical teams;
assurance functions;
legal and compliance functions;
security functions; and
operational teams.

10.3 Responsibility Model

Governance Authority
       ↓
Accountable Executive
       ↓
AI Governance Function
       ↓
System Owner
       ↓
Risk / Control Owners
       ↓
Operational Personnel

11. Competence Implementation

11.1 Purpose

Personnel performing AI governance activities should have appropriate competence.

11.2 Competence Areas

Competence may include:

AI governance;
risk management;
AI technical knowledge;
data governance;
security;
privacy;
regulatory requirements;
assurance;
incident management; and
lifecycle management.

11.3 Competence Flow

Role
   ↓
Competence Requirement
   ↓
Competence Assessment
   ↓
Training / Development
   ↓
Competence Evidence
   ↓
Periodic Review

12. Awareness Implementation

12.1 Purpose

Relevant personnel should understand applicable AI governance requirements and their responsibilities.

12.2 Awareness Activities

Activities may include:

induction;
role-specific training;
awareness sessions;
policy communication;
incident lessons;
governance updates; and
periodic refreshers.

12.3 Awareness Flow

Governance Requirement
       ↓
Awareness Content
       ↓
Communication
       ↓
Personnel Understanding
       ↓
Evidence
       ↓
Review

13. Communication Implementation

13.1 Purpose

AI governance information should be communicated to relevant internal and external stakeholders as appropriate.

13.2 Communication Areas

Communication may address:

governance policy;
responsibilities;
AI system status;
significant risks;
incidents;
changes;
assurance findings; and
management decisions.

13.3 Communication Flow

Governance Information
       ↓
Communication Requirement
       ↓
Audience
       ↓
Communication Method
       ↓
Communication Record
       ↓
Feedback / Action

14. Documentation Implementation

14.1 Purpose

The organization should maintain documented information necessary to operate and demonstrate the AI management system.

14.2 AIGO Documentation Layers

Framework Documents
       ↓
Implementation Guidance
       ↓
Procedures
       ↓
Templates
       ↓
Records
       ↓
Evidence

14.3 Documentation Control

Documentation should be:

identified;
version controlled;
reviewed;
approved;
accessible;
protected; and
appropriately retained.

15. AI System Registration Implementation

15.1 Purpose

Every applicable AI system should enter the AIGO governance lifecycle through a controlled registration process.

15.2 Registration Activities

Implementation may include:

system identification;
owner assignment;
purpose definition;
intended-use identification;
data identification;
lifecycle stage;
third-party dependencies;
initial risk information; and
classification.

15.3 Registration Flow

AI System Identified
       ↓
Registration
       ↓
System Profile
       ↓
Owner Assignment
       ↓
Classification
       ↓
Risk Assessment
       ↓
Governance Lifecycle

16. AI System Classification Implementation

16.1 Purpose

Classification determines the governance requirements applicable to an AI system.

16.2 Classification Factors

Factors may include:

intended use;
potential impact;
autonomy;
affected persons;
criticality;
data sensitivity;
regulatory requirements;
risk; and
organizational policy.

16.3 Classification Flow

AI System
   ↓
Classification Criteria
   ↓
Assessment
   ↓
Classification
   ↓
Governance Requirements
   ↓
Control Requirements

17. AI Risk Management Implementation

17.1 Purpose

AI risk management should be integrated into the AI lifecycle.

17.2 Risk Activities

Implementation should address:

risk identification;
risk analysis;
risk evaluation;
treatment;
residual risk;
acceptance;
monitoring; and
review.

17.3 Risk Flow

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

18. AI Impact Assessment Implementation

18.1 Purpose

Where appropriate, the organization should evaluate potential impacts associated with AI systems.

18.2 Impact Areas

Impact assessment may consider:

individuals;
groups;
organizations;
society;
safety;
privacy;
security;
rights;
fairness;
operational continuity; and
economic impact.

18.3 Impact Assessment Flow

AI System
   ↓
Impact Identification
   ↓
Affected Stakeholders
   ↓
Impact Analysis
   ↓
Mitigation
   ↓
Residual Impact
   ↓
Decision

19. Risk Treatment Implementation

19.1 Purpose

Risk treatment converts risk decisions into operational actions.

19.2 Treatment Options

Treatment may include:

mitigation;
modification;
transfer;
avoidance;
acceptance;
additional controls; or
discontinuation.

19.3 Treatment Flow

Risk
   ↓
Treatment Decision
   ↓
Control Selection
   ↓
Implementation
   ↓
Verification
   ↓
Residual Risk

20. Control Implementation

20.1 Purpose

Controls translate governance requirements into practical safeguards.

20.2 Control Lifecycle

Control Requirement
       ↓
Control Design
       ↓
Control Assignment
       ↓
Implementation
       ↓
Testing
       ↓
Monitoring
       ↓
Effectiveness Assessment
       ↓
Improvement

20.3 Control Ownership

Each material control should have an identifiable owner responsible for implementation and ongoing effectiveness.

21. Operational Planning Implementation

21.1 Purpose

AI governance activities should be planned and integrated into operational processes.

21.2 Planning Areas

Planning may include:

objectives;
activities;
responsibilities;
resources;
dependencies;
timelines;
controls;
evidence; and
performance indicators.

21.3 Planning Model

Objective
   ↓
Requirement
   ↓
Activity
   ↓
Owner
   ↓
Resource
   ↓
Timeline
   ↓
Evidence
   ↓
Performance Measure

22. AI Lifecycle Implementation

22.1 Purpose

AIGO applies governance throughout the AI system lifecycle.

22.2 Lifecycle

Govern
   ↓
Identify
   ↓
Classify
   ↓
Assess
   ↓
Treat
   ↓
Approve
   ↓
Deploy
   ↓
Operate
   ↓
Monitor
   ↓
Assure
   ↓
Improve
   ↓
Change / Continue / Retire

22.3 Lifecycle Principle

Governance activities should be proportionate to the AI system’s lifecycle stage, risk, and impact.

23. Development and Acquisition Implementation

23.1 Purpose

AI systems may be internally developed, externally acquired, or obtained as services.

23.2 Implementation Requirements

The organization should consider:

requirements;
supplier assessment;
technical evaluation;
risk assessment;
security;
privacy;
testing;
contractual requirements;
approval; and
monitoring.

23.3 Acquisition Flow

Need
   ↓
Requirements
   ↓
Supplier / Solution Assessment
   ↓
Risk Assessment
   ↓
Evaluation
   ↓
Approval
   ↓
Acquisition / Development

24. Data Governance Implementation

24.1 Purpose

AI implementation should address data-related governance requirements appropriate to the AI system.

24.2 Data Governance Areas

These may include:

data sources;
data quality;
data provenance;
data suitability;
data access;
data protection;
data retention;
data lineage; and
data use restrictions.

24.3 Data Governance Flow

Data Source
   ↓
Data Assessment
   ↓
Data Governance
   ↓
Data Preparation
   ↓
AI System
   ↓
Monitoring

25. Technical Implementation

25.1 Purpose

Technical implementation should translate governance and risk requirements into system-level controls.

25.2 Technical Areas

Depending on the AI system, implementation may include:

model controls;
access controls;
security;
logging;
monitoring;
testing;
validation;
human oversight;
resilience; and
change control.

25.3 Technical Governance Flow

Governance Requirement
       ↓
Technical Requirement
       ↓
System Control
       ↓
Testing
       ↓
Deployment
       ↓
Monitoring

26. Human Oversight Implementation

26.1 Purpose

Where human oversight is required, the organization should establish appropriate responsibilities, authorities, and intervention mechanisms.

26.2 Human Oversight Areas

Implementation may define:

oversight role;
decision authority;
intervention conditions;
escalation;
override mechanisms;
review frequency; and
evidence requirements.

26.3 Oversight Flow

AI System Output
       ↓
Human Oversight
       ↓
Review
       ↓
Accept / Override / Escalate
       ↓
Decision
       ↓
Evidence

27. AI System Validation and Testing

27.1 Purpose

AI systems should be evaluated against applicable requirements before and, where appropriate, after deployment.

27.2 Testing Areas

Testing may include:

functionality;
performance;
robustness;
security;
reliability;
fairness;
safety;
privacy;
data quality; and
operational suitability.

27.3 Testing Flow

Requirement
   ↓
Test Objective
   ↓
Test Design
   ↓
Test Execution
   ↓
Result
   ↓
Deficiency / Pass
   ↓
Approval / Remediation

28. Deployment Approval Implementation

28.1 Purpose

Deployment should occur only after required governance conditions have been satisfied.

28.2 Deployment Readiness

The readiness assessment may consider:

risk status;
control status;
testing;
monitoring;
human oversight;
documentation;
incident readiness;
approval; and
residual risk.

28.3 Deployment Flow

Implementation Complete
       ↓
Readiness Assessment
       ↓
Risk Review
       ↓
Control Review
       ↓
Approval
       ↓
Deployment
       ↓
Post-Deployment Verification

29. Operational Monitoring Implementation

29.1 Purpose

Monitoring determines whether AI systems continue to operate within approved governance and risk conditions.

29.2 Monitoring Areas

Monitoring may include:

performance;
reliability;
security;
incidents;
risk indicators;
control effectiveness;
drift;
stakeholder feedback; and
compliance.

29.3 Monitoring Flow

AI System
   ↓
Indicator
   ↓
Measurement
   ↓
Threshold
   ↓
Alert / Review
   ↓
Action
   ↓
Evidence

30. AI Incident Management Implementation

30.1 Purpose

The organization should identify, record, investigate, respond to, and learn from AI-related incidents.

30.2 Incident Lifecycle

Detection
   ↓
Recording
   ↓
Classification
   ↓
Containment
   ↓
Investigation
   ↓
Response
   ↓
Root Cause
   ↓
Corrective Action
   ↓
Closure
   ↓
Lessons Learned

31. Change Management Implementation

31.1 Purpose

Material AI system changes should be assessed and governed before implementation.

31.2 Change Areas

Changes may include:

model;
data;
architecture;
supplier;
intended use;
deployment environment;
security;
controls;
governance requirements; or
organizational context.

31.3 Change Flow

Change Request
       ↓
Change Classification
       ↓
Impact Assessment
       ↓
Risk Assessment
       ↓
Approval
       ↓
Implementation
       ↓
Validation
       ↓
Post-Change Monitoring

32. AI Assurance Implementation

32.1 Purpose

Assurance provides structured evaluation of whether governance requirements and controls are implemented and effective.

32.2 Assurance Activities

Assurance may include:

review;
testing;
sampling;
interviews;
evidence examination;
technical assessment;
control testing; and
independent evaluation.

32.3 Assurance Flow

Assurance Scope
       ↓
Criteria
       ↓
Evidence
       ↓
Testing
       ↓
Findings
       ↓
Conclusion
       ↓
Management Response

33. Internal Audit and Evaluation Implementation

33.1 Purpose

Where applicable, internal audit or equivalent evaluation activities should assess the AI management system.

33.2 Audit Activities

Activities may include:

audit planning;
scope definition;
criteria;
evidence collection;
testing;
findings;
reporting; and
follow-up.

33.3 Audit Flow

Audit Plan
   ↓
Scope
   ↓
Criteria
   ↓
Evidence
   ↓
Testing
   ↓
Findings
   ↓
Report
   ↓
Follow-Up

34. Management Review Implementation

34.1 Purpose

Management review evaluates the continuing suitability, adequacy, effectiveness, and performance of the AI management system.

34.2 Inputs

Management review may consider:

governance performance;
risks;
monitoring;
incidents;
assurance;
audit findings;
corrective actions;
stakeholder feedback;
changes; and
improvement opportunities.

34.3 Management Review Flow

Governance Information
       ↓
Management Review Inputs
       ↓
Management Review
       ↓
Decisions
       ↓
Actions
       ↓
Assigned Owners
       ↓
Follow-Up

35. Corrective Action Implementation

35.1 Purpose

Corrective action addresses identified nonconformities, control deficiencies, evidence gaps, and other governance weaknesses.

35.2 Corrective Action Lifecycle

Finding
   ↓
Containment
   ↓
Root Cause Analysis
   ↓
Corrective Action
   ↓
Implementation
   ↓
Effectiveness Assessment
   ↓
Closure

36. Continual Improvement Implementation

36.1 Purpose

AIGO should continually improve the AI governance management system based on evidence, experience, performance, risks, and changes.

36.2 Improvement Inputs

Inputs may include:

monitoring;
incidents;
assurance;
audits;
management review;
stakeholder feedback;
regulatory changes;
technology changes; and
lessons learned.

36.3 Improvement Flow

Performance
   ↓
Evaluation
   ↓
Improvement Opportunity
   ↓
Prioritization
   ↓
Decision
   ↓
Implementation
   ↓
Verification
   ↓
Standardization

37. Evidence Implementation

37.1 Purpose

Evidence provides objective support for demonstrating implementation and operation.

37.2 Evidence Sources

Evidence may originate from:

governance records;
system records;
assessments;
approvals;
monitoring;
incidents;
changes;
assurance;
management reviews; and
corrective actions.

37.3 Evidence Flow

Requirement
   ↓
Activity
   ↓
Record
   ↓
Evidence
   ↓
Validation
   ↓
Assurance

38. Evidence Repository Implementation

38.1 Purpose

The organization should establish controlled mechanisms for storing and retrieving evidence.

38.2 Repository Requirements

The repository should support:

identification;
ownership;
classification;
access control;
versioning;
retention;
backup;
retrieval; and
disposal.

38.3 Repository Flow

Evidence
   ↓
Metadata
   ↓
Classification
   ↓
Repository
   ↓
Controlled Access
   ↓
Retention

39. Performance Evaluation Implementation

39.1 Purpose

The organization should evaluate whether governance processes achieve intended outcomes.

39.2 Performance Areas

Performance evaluation may include:

governance objectives;
risk performance;
control effectiveness;
monitoring indicators;
incident trends;
assurance findings;
corrective-action performance; and
improvement performance.

39.3 Performance Flow

Objective
   ↓
Indicator
   ↓
Measurement
   ↓
Analysis
   ↓
Evaluation
   ↓
Management Decision

40. KPI and KRI Implementation

40.1 Key Performance Indicators

KPIs may include:

registration completion;
risk-assessment completion;
control-assessment completion;
monitoring coverage;
training completion;
corrective-action closure; and
assurance completion.

40.2 Key Risk Indicators

KRIs may include:

high-risk AI systems;
unresolved high risks;
incidents;
control failures;
overdue assessments;
significant changes; and
emerging risks.

40.3 Indicator Flow

Governance Objective
       ↓
KPI / KRI
       ↓
Data Collection
       ↓
Measurement
       ↓
Threshold
       ↓
Management Response

41. Resource Implementation

41.1 Purpose

The organization should provide resources necessary to establish and operate AI governance.

41.2 Resource Areas

Resources may include:

personnel;
expertise;
technology;
governance systems;
assessment tools;
monitoring tools;
assurance resources; and
training.

41.3 Resource Flow

Governance Requirement
       ↓
Resource Requirement
       ↓
Capacity Assessment
       ↓
Resource Allocation
       ↓
Implementation
       ↓
Performance Review

42. Third-Party Implementation

42.1 Purpose

Third-party AI services and suppliers should be incorporated into governance where applicable.

42.2 Third-Party Controls

Implementation may address:

supplier selection;
due diligence;
contractual requirements;
risk assessment;
security;
data governance;
service monitoring;
incidents;
changes; and
exit arrangements.

42.3 Third-Party Flow

Third Party
   ↓
Due Diligence
   ↓
Risk Assessment
   ↓
Contract
   ↓
Implementation
   ↓
Monitoring
   ↓
Assurance
   ↓
Exit / Renewal

43. Outsourced Process Implementation

43.1 Purpose

Where AI governance processes are outsourced, the organization should retain appropriate oversight and accountability.

43.2 Oversight Areas

The organization should define:

responsibility;
service requirements;
evidence;
performance;
monitoring;
assurance;
escalation; and
change management.

44. Regulatory Implementation

44.1 Purpose

The AIGO implementation should identify and address applicable regulatory requirements.

44.2 Regulatory Flow

Regulation
   ↓
Applicability Assessment
   ↓
Requirement
   ↓
AIGO Mapping
   ↓
Control / Procedure
   ↓
Evidence
   ↓
Monitoring

44.3 Regulatory Change

Regulatory changes should be assessed for their potential effect on:

governance;
AI systems;
risk;
controls;
procedures;
evidence; and
reporting.

45. Implementation Traceability

45.1 Purpose

Implementation traceability ensures that every material requirement can be connected to an operational implementation.

45.2 Traceability Model

ISO/IEC 42001 Requirement
       ↓
AIGO Mapping
       ↓
Implementation Requirement
       ↓
Procedure
       ↓
Control
       ↓
Responsible Role
       ↓
Evidence
       ↓
Monitoring
       ↓
Assurance

45.3 Traceability Principle

Implementation should be demonstrable rather than assumed.

46. Implementation Readiness Assessment

46.1 Purpose

Before operationalizing an AIGO implementation area, the organization should assess readiness.

46.2 Readiness Factors

Readiness may include:

governance;
roles;
procedures;
controls;
resources;
competence;
technology;
evidence;
monitoring; and
assurance.

46.3 Readiness Flow

Requirement
   ↓
Implementation Design
   ↓
Readiness Assessment
   ↓
Gap?
 ↙      ↘
No       Yes
 ↓        ↓
Deploy   Remediate

47. Implementation Gap Management

47.1 Purpose

Implementation gaps identify areas where intended governance arrangements are not yet operational.

47.2 Gap Categories

Examples include:

missing procedure;
missing control;
missing owner;
insufficient resources;
missing evidence;
incomplete implementation;
ineffective control; or
inadequate monitoring.

47.3 Gap Flow

Expected State
      ↓
Current State
      ↓
Gap Analysis
      ↓
Gap Classification
      ↓
Remediation Plan
      ↓
Implementation
      ↓
Verification

48. Implementation Prioritization

48.1 Purpose

Implementation should be prioritized according to organizational risk and governance significance.

48.2 Prioritization Factors

Factors may include:

AI risk;
regulatory urgency;
business criticality;
stakeholder impact;
control dependency;
implementation complexity;
resource availability; and
management priorities.

48.3 Prioritization Model

Implementation Candidate
       ↓
Risk / Impact
       ↓
Urgency
       ↓
Dependency
       ↓
Resource Assessment
       ↓
Priority
       ↓
Implementation Plan

49. Implementation Roadmap

49.1 Purpose

The implementation roadmap provides a structured sequence for establishing AIGO capabilities.

49.2 Recommended Sequence

1. Governance Foundation
       ↓
2. Scope and Context
       ↓
3. Roles and Responsibilities
       ↓
4. AI System Registration
       ↓
5. Classification
       ↓
6. Risk Management
       ↓
7. Controls
       ↓
8. Procedures
       ↓
9. Evidence
       ↓
10. Monitoring
       ↓
11. Assurance
       ↓
12. Management Review
       ↓
13. Continual Improvement

49.3 Implementation Principle

The sequence may be adapted according to organizational context and existing capabilities.

50. Implementation Phases

50.1 Phase 1 — Foundation

Establish:

governance;
scope;
policy;
roles;
terminology;
lifecycle;
risk model; and
control architecture.

50.2 Phase 2 — Operationalization

Establish:

registration;
classification;
assessments;
approvals;
procedures;
controls; and
evidence mechanisms.

50.3 Phase 3 — Operation

Operate:

lifecycle governance;
monitoring;
incident management;
change management;
risk management; and
assurance.

50.4 Phase 4 — Optimization

Establish:

performance analytics;
maturity measurement;
continual improvement;
management-system optimization; and
advanced assurance.

51. Implementation Maturity

51.1 Maturity Levels

AIGO implementation maturity may be evaluated across the following levels:

Level	Description
1 — Initial	Governance is largely informal or reactive
2 — Developing	Basic processes are established
3 — Defined	Processes are documented and consistently applied
4 — Managed	Performance and effectiveness are measured
5 — Optimized	Governance is continuously improved and optimized

51.2 Maturity Flow

Initial
   ↓
Developing
   ↓
Defined
   ↓
Managed
   ↓
Optimized

52. Implementation Assessment

52.1 Purpose

Implementation assessment determines whether AIGO capabilities have been established and are operating as intended.

52.2 Assessment Areas

Assessment may examine:

governance;
process;
roles;
controls;
evidence;
performance;
monitoring;
assurance; and
improvement.

52.3 Assessment Flow

Implementation Requirement
       ↓
Assessment Criteria
       ↓
Evidence
       ↓
Assessment
       ↓
Result
       ↓
Gap / Conformity
       ↓
Improvement

53. Implementation Assurance

53.1 Purpose

Assurance provides confidence that implementation is not merely documented but is functioning in practice.

53.2 Assurance Layers

First Line
Operational Implementation
       ↓
Second Line
Governance / Risk Oversight
       ↓
Third Line
Independent Assurance

53.3 Assurance Principle

The depth and independence of assurance should be proportionate to risk and organizational requirements.

54. Implementation Evidence

54.1 Evidence Requirements

Implementation evidence should demonstrate:

requirement interpretation;
responsibility;
implementation;
operation;
review;
effectiveness; and
improvement.

54.2 Evidence Flow

Implementation Requirement
       ↓
Implementation Activity
       ↓
Record
       ↓
Evidence
       ↓
Verification
       ↓
Assurance

55. Implementation Reporting

55.1 Purpose

Implementation progress should be reported to appropriate governance authorities.

55.2 Reporting Areas

Reports may include:

implementation status;
completed capabilities;
open gaps;
risks;
dependencies;
resources;
evidence status;
assurance findings; and
next actions.

55.3 Reporting Flow

Implementation Data
       ↓
Analysis
       ↓
Implementation Report
       ↓
Governance Review
       ↓
Decision
       ↓
Action

56. Implementation Dependencies

56.1 Purpose

AIGO implementation components may depend on one another.

56.2 Example Dependencies

Governance
   ↓
Roles
   ↓
Registration
   ↓
Classification
   ↓
Risk
   ↓
Controls
   ↓
Procedures
   ↓
Evidence
   ↓
Monitoring
   ↓
Assurance
   ↓
Improvement

56.3 Dependency Principle

Changes to foundational components should trigger an assessment of dependent implementation components.

57. Implementation Change Control

57.1 Purpose

Changes to the AIGO implementation architecture should themselves be controlled.

57.2 Change Areas

Changes may include:

framework requirements;
procedures;
controls;
roles;
system architecture;
mapping;
evidence requirements;
monitoring;
assurance; and
implementation tools.

57.3 Change Flow

Implementation Change
       ↓
Impact Assessment
       ↓
Risk Assessment
       ↓
Approval
       ↓
Implementation
       ↓
Validation
       ↓
Documentation Update

58. Implementation Configuration Management

58.1 Purpose

The organization should maintain awareness of the approved configuration of its AI governance system.

58.2 Configuration Elements

These may include:

framework version;
applicable procedures;
controls;
system profiles;
mappings;
templates;
evidence requirements; and
monitoring requirements.

58.3 Configuration Flow

Approved Governance Baseline
       ↓
Configuration
       ↓
Change
       ↓
Review
       ↓
Approval
       ↓
Updated Baseline

59. Implementation and Continuous Improvement

Implementation does not end when the initial framework is deployed. The operating model should continuously evaluate:

effectiveness;
relevance;
risk;
performance;
stakeholder expectations;
regulatory developments;
technology;
incidents; and
lessons learned.

Implement
   ↓
Operate
   ↓
Measure
   ↓
Review
   ↓
Improve
   ↓
Re-Implement

60. Implementation Operating Model

The complete AIGO implementation operating model can be represented as:

Governance
     ↓
Context
     ↓
Scope
     ↓
Roles
     ↓
AI System Registration
     ↓
Classification
     ↓
Risk Assessment
     ↓
Risk Treatment
     ↓
Controls
     ↓
Approval
     ↓
Deployment
     ↓
Operation
     ↓
Monitoring
     ↓
Incident / Change Management
     ↓
Assurance
     ↓
Management Review
     ↓
Corrective Action
     ↓
Continual Improvement

61. Implementation-to-Evidence Model

Implementation should produce evidence at every material stage.

Requirement
   ↓
Implementation
   ↓
Responsible Role
   ↓
Operational Record
   ↓
Evidence
   ↓
Monitoring
   ↓
Assurance
   ↓
Management Review

62. Implementation-to-Control Model

Every material implementation requirement should be connected to an applicable control.

Implementation Area	Control Relationship
Governance	Governance controls
Registration	System registration controls
Classification	Classification controls
Risk	Risk-management controls
Lifecycle	Lifecycle controls
Deployment	Approval and deployment controls
Operation	Operational controls
Monitoring	Monitoring controls
Incidents	Incident controls
Changes	Change controls
Assurance	Assurance controls
Improvement	Continual-improvement controls

63. Implementation-to-Procedure Model

Procedures operationalize the AIGO framework.

Framework Requirement
       ↓
Implementation Guidance
       ↓
Procedure
       ↓
Responsible Role
       ↓
Activity
       ↓
Record
       ↓
Evidence

The procedures in guidance\02-procedures provide the operational execution layer for the implementation architecture.

64. Implementation-to-Template Model

Templates provide standardized mechanisms for executing repeatable governance activities.

Procedure
   ↓
Required Activity
   ↓
Template
   ↓
Completed Record
   ↓
Evidence

Templates should therefore be finalized after the framework, guidance, procedures, mappings, and control architecture have reached sufficient maturity.

65. Implementation-to-Tooling Model

Tools may support:

registration;
assessment;
workflow;
evidence;
monitoring;
reporting;
risk management;
control management;
assurance; and
document management.

Governance Requirement
       ↓
Process
       ↓
Tool Requirement
       ↓
Tool Configuration
       ↓
Operational Use
       ↓
Evidence

66. Implementation Integration Model

The AIGO repository structure should operate as an integrated documentation architecture.

framework
   ↓
guidance
   ↓
mappings
   ↓
schemas
   ↓
tools
   ↓
operational evidence

Each layer should reference and support the others.

67. ISO/IEC 42001 Implementation Traceability Matrix

ISO/IEC 42001 Area	AIGO Implementation Layer	Primary Artifacts
Context	Framework / Governance	Charter, domains, scope
Leadership	Governance	Roles, policy, governance
Planning	Risk / Controls	Risk and control framework
Support	Guidance	Implementation guidance, procedures
Operation	Procedures	Operational procedures
Performance Evaluation	Monitoring / Assurance	Monitoring and assurance procedures
Improvement	Continuous Improvement	Improvement guidance and procedure
AI Lifecycle	Lifecycle	Lifecycle framework and implementation
Risk Management	Risk	Risk framework and procedures
Evidence	Evidence Mapping	Evidence mapping and records

68. Implementation Governance Cycle

The implementation architecture should operate as a controlled cycle.

Plan
   ↓
Implement
   ↓
Operate
   ↓
Monitor
   ↓
Assure
   ↓
Review
   ↓
Improve
   ↓
Plan

This cycle supports continual alignment between the AI management system and the organization’s changing context.

69. Implementation Review

Implementation should be periodically reviewed to determine whether:

requirements remain applicable;
implementation remains effective;
controls remain appropriate;
procedures remain usable;
evidence remains sufficient;
monitoring remains meaningful;
assurance remains adequate; and
improvement actions are effective.

Implementation
       ↓
Review
       ↓
Effectiveness
       ↓
Gap / Opportunity
       ↓
Action
       ↓
Updated Implementation

70. Implementation Completion Criteria

An implementation area may be considered operational when:

requirements are understood;
responsibilities are assigned;
processes are defined;
procedures exist;
controls are implemented;
personnel are competent;
required evidence is generated;
monitoring is established;
assurance is possible; and
improvement mechanisms exist.

71. Implementation Readiness Checklist

A practical readiness assessment should consider:

72. Implementation Dependencies with Existing AIGO Repository

The current repository structure supports the implementation architecture as follows:

framework
   ↓
Governance Foundation

guidance\01-implementation
   ↓
Implementation Guidance

guidance\02-procedures
   ↓
Operational Procedures

guidance\03-templates
   ↓
Standardized Records

guidance\04-examples
   ↓
Practical Demonstrations

mappings
   ↓
External Framework Alignment

schemas
   ↓
Structured Data Definitions

tools
   ↓
Operational Enablement

The implementation mapping therefore connects the existing repository layers rather than creating an independent implementation system.

73. Implementation Priority Model

Implementation priorities should generally follow:

Governance Foundation
       ↓
Legal / Regulatory Obligations
       ↓
High-Risk AI Systems
       ↓
Critical Controls
       ↓
Operational Processes
       ↓
Monitoring
       ↓
Assurance
       ↓
Optimization

This allows the organization to establish the highest-value governance capabilities first.

74. Implementation Risk Model

Implementation itself carries risks. Examples include:

unclear ownership;
incomplete scope;
inadequate resources;
inconsistent procedures;
ineffective controls;
insufficient evidence;
fragmented tooling;
weak monitoring;
insufficient assurance; and
uncontrolled change.

Implementation risks should therefore be managed through the AIGO risk-management process.

75. Implementation Success Factors

Successful implementation depends on:

executive sponsorship;
clear governance;
defined accountability;
practical procedures;
risk-based prioritization;
effective controls;
competent personnel;
reliable evidence;
meaningful monitoring;
independent assurance; and
continual improvement.

76. Implementation Failure Indicators

Potential indicators of weak implementation include:

documentation exists but is not used;
responsibilities are unclear;
risk assessments are incomplete;
controls are not tested;
evidence cannot be retrieved;
monitoring produces no actionable information;
incidents are not incorporated into improvement;
management review is ineffective;
corrective actions remain open; or
procedures do not reflect actual operations.

77. Implementation Effectiveness Model

Implementation effectiveness can be evaluated across four dimensions:

Dimension	Question
Design	Is the governance arrangement appropriately designed?
Implementation	Has it actually been established?
Operation	Is it being used consistently?
Effectiveness	Is it achieving the intended outcome?

Design
   ↓
Implementation
   ↓
Operation
   ↓
Effectiveness
   ↓
Improvement

78. Implementation Maturity Assessment Model

AIGO implementation maturity may be assessed across:

governance;
process;
risk;
controls;
evidence;
monitoring;
assurance; and
improvement.

Capability
   ↓
Repeatability
   ↓
Consistency
   ↓
Measurement
   ↓
Assurance
   ↓
Optimization

79. Implementation and Certification Readiness

Where an organization intends to pursue external conformity assessment or certification against ISO/IEC 42001, implementation should support:

defined scope;
documented information;
operational evidence;
risk management;
control implementation;
performance evaluation;
management review;
corrective action; and
continual improvement.

AIGO implementation should therefore be designed to support both operational governance and objective demonstration of the implemented management system.

80. Implementation Evidence Package

A consolidated implementation evidence package may include:

Scope
   ↓
Governance Policy
   ↓
Roles
   ↓
Risk Assessment
   ↓
Control Assessment
   ↓
Procedures
   ↓
Operational Records
   ↓
Monitoring
   ↓
Incidents / Changes
   ↓
Assurance
   ↓
Management Review
   ↓
Corrective Actions
   ↓
Improvement Records

This package should provide sufficient traceability to demonstrate implementation without unnecessarily duplicating operational records.

81. Implementation Governance Dashboard

A governance dashboard may monitor:

Area	Example Indicator
Registration	Registered AI systems
Classification	Classification completion
Risk	Open high risks
Controls	Control effectiveness
Evidence	Evidence completeness
Monitoring	Monitoring coverage
Incidents	Open incidents
Changes	Material changes
Assurance	Open findings
Corrective Action	Overdue actions
Improvement	Improvement completion

82. Implementation Reporting Cadence

Reporting frequency should be proportionate to risk and organizational requirements. Possible reporting levels include:

operational reporting;
monthly governance reporting;
quarterly management review;
annual management-system review; and
event-triggered reporting.

The actual cadence should be defined by the organization’s governance arrangements.

83. Implementation and Management Review Inputs

Implementation performance should contribute to management review.

Implementation Status
       ↓
Performance
       ↓
Risks
       ↓
Controls
       ↓
Incidents
       ↓
Assurance
       ↓
Improvement
       ↓
Management Review

84. Implementation Change Triggers

A review of the implementation architecture should be triggered by:

material regulatory changes;
significant organizational changes;
new AI technologies;
major AI system changes;
significant incidents;
assurance findings;
recurring control failures;
major stakeholder changes; or
changes in organizational risk appetite.

85. Implementation Control Points

Key implementation control points include:

Registration
   ↓
Classification
   ↓
Risk Assessment
   ↓
Control Selection
   ↓
Approval
   ↓
Deployment
   ↓
Monitoring
   ↓
Assurance
   ↓
Retirement

Each control point should have defined:

criteria;
accountable role;
decision authority;
evidence;
escalation; and
review requirements.

86. Implementation Decision Model

Governance decisions should follow a consistent model.

Decision Required
       ↓
Context
       ↓
Evidence
       ↓
Risk
       ↓
Options
       ↓
Recommendation
       ↓
Authorized Decision
       ↓
Conditions
       ↓
Record
       ↓
Follow-Up

87. Implementation Escalation Model

Material issues should be escalated according to defined thresholds.

Issue
   ↓
Assessment
   ↓
Threshold Check
   ↓
Within Authority?
 ↙           ↘
Yes           No
 ↓             ↓
Resolve      Escalate
                ↓
        Governance Authority
                ↓
              Decision

88. Implementation Exception Management

Exceptions should be:

justified;
risk assessed;
documented;
approved;
time-bound where appropriate;
monitored; and
reviewed.

Exception Request
       ↓
Assessment
       ↓
Risk Evaluation
       ↓
Approval
       ↓
Compensating Controls
       ↓
Monitoring
       ↓
Closure / Renewal

89. Implementation and Risk Acceptance

Risk acceptance should be performed by an appropriately authorized role.

Residual Risk
       ↓
Risk Acceptance Assessment
       ↓
Authority Check
       ↓
Decision
 ↙             ↘
Accept       Reject / Treat
 ↓               ↓
Record          Action

Risk acceptance should not be used as a substitute for required risk treatment where treatment is reasonably practicable and required by organizational policy.

90. Implementation and AI Retirement

Implementation should include controlled retirement of AI systems.

Retirement Trigger
       ↓
Risk Assessment
       ↓
Approval
       ↓
Decommissioning
       ↓
Data / Access Disposition
       ↓
Verification
       ↓
Closure
       ↓
Evidence

Retirement should preserve relevant evidence for the applicable retention period.

91. End-to-End AIGO Implementation Model

The complete implementation architecture can be represented as:

Organizational Context
        ↓
Governance
        ↓
Scope
        ↓
Roles
        ↓
AI System Registration
        ↓
Classification
        ↓
Risk Management
        ↓
Impact Assessment
        ↓
Control Selection
        ↓
Implementation
        ↓
Approval
        ↓
Deployment
        ↓
Operation
        ↓
Monitoring
        ↓
Incident / Change Management
        ↓
Assurance
        ↓
Management Review
        ↓
Corrective Action
        ↓
Continual Improvement
        ↓
Retirement

92. AIGO Implementation Traceability Model

The implementation traceability chain is:

ISO/IEC 42001
       ↓
AIGO Framework
       ↓
AIGO Guidance
       ↓
AIGO Procedure
       ↓
AIGO Control
       ↓
AIGO Role
       ↓
Operational Activity
       ↓
Evidence
       ↓
Monitoring
       ↓
Assurance
       ↓
Management Review
       ↓
Improvement

This chain provides the central implementation relationship between the external management-system standard and the AIGO operational framework.

93. Implementation Document Control

93.1 Controlled Information

Field	Value
Document Title	AIGO — ISO/IEC 42001 Implementation Mapping
Document ID	`AIGO-MAP-ISO42001-008`
Version	0.1
Status	Draft
Framework	AIGO AI Governance Operating Framework
Mapping Standard	ISO/IEC 42001
Mapping Domain	Implementation
Primary Owner
Technical Reviewer
Governance Reviewer
Approver
Effective Date
Next Review Date

93.2 Document Control Requirements

This document should be maintained under the AIGO document-control process. Material changes should be:

identified;
reviewed;
approved;
versioned;
recorded; and
communicated where necessary.

94. Final Implementation Control Statement

The AIGO implementation mapping establishes the relationship between ISO/IEC 42001 management-system requirements and the operational architecture of the AIGO AI Governance Operating Framework. It provides the implementation bridge connecting:

Requirement
   ↓
Framework
   ↓
Guidance
   ↓
Procedure
   ↓
Control
   ↓
Role
   ↓
Activity
   ↓
Evidence
   ↓
Monitoring
   ↓
Assurance
   ↓
Management Review
   ↓
Improvement

The implementation model is intended to support a controlled, risk-based, evidence-driven, and continually improving AI governance operating environment.

95. End of Mapping Document

95.1 Final Status

AIGO — ISO/IEC 42001 Implementation Mapping Document ID: AIGO-MAP-ISO42001-008 Version: 0.1 Status: Draft Mapping Standard: ISO/IEC 42001 Mapping Type: Implementation Mapping End of Document

​AIGO — ISO/IEC 42001 Implementation Mapping

​AIGO — AI Governance Operating Framework

​1. Purpose

​2. Scope

​3. Implementation Principle

​4. AIGO Implementation Architecture

​5. ISO/IEC 42001 Implementation Relationship

​6. Organizational Context Implementation

​6.1 Purpose

​6.2 Implementation Activities

​6.3 Implementation Flow

​7. Scope Implementation

​7.1 Purpose

​7.2 Scope Considerations

​7.3 Scope Model

​8. Leadership Implementation

​8.1 Purpose

​8.2 Leadership Activities

​8.3 Leadership Flow

​9. AI Governance Policy Implementation

​9.1 Purpose

​9.2 Implementation Requirements

​9.3 Policy Implementation Flow

​10. Governance Roles Implementation

​10.1 Purpose

​10.2 Role Categories

​10.3 Responsibility Model

​11. Competence Implementation

​11.1 Purpose

​11.2 Competence Areas

​11.3 Competence Flow

​12. Awareness Implementation

​12.1 Purpose

​12.2 Awareness Activities

​12.3 Awareness Flow

​13. Communication Implementation

​13.1 Purpose

​13.2 Communication Areas

​13.3 Communication Flow

​14. Documentation Implementation

​14.1 Purpose

​14.2 AIGO Documentation Layers

​14.3 Documentation Control

​15. AI System Registration Implementation

​15.1 Purpose

​15.2 Registration Activities

​15.3 Registration Flow

​16. AI System Classification Implementation

​16.1 Purpose

​16.2 Classification Factors

​16.3 Classification Flow

​17. AI Risk Management Implementation

​17.1 Purpose

​17.2 Risk Activities

​17.3 Risk Flow

​18. AI Impact Assessment Implementation

​18.1 Purpose

​18.2 Impact Areas

​18.3 Impact Assessment Flow

​19. Risk Treatment Implementation

​19.1 Purpose

​19.2 Treatment Options

​19.3 Treatment Flow

​20. Control Implementation

​20.1 Purpose

​20.2 Control Lifecycle

​20.3 Control Ownership

​21. Operational Planning Implementation

​21.1 Purpose

​21.2 Planning Areas

​21.3 Planning Model

​22. AI Lifecycle Implementation

​22.1 Purpose

​22.2 Lifecycle

​22.3 Lifecycle Principle

​23. Development and Acquisition Implementation

​23.1 Purpose

​23.2 Implementation Requirements

​23.3 Acquisition Flow

​24. Data Governance Implementation

AIGO — ISO/IEC 42001 Implementation Mapping

AIGO — AI Governance Operating Framework

1. Purpose

2. Scope

3. Implementation Principle

4. AIGO Implementation Architecture

5. ISO/IEC 42001 Implementation Relationship

6. Organizational Context Implementation

6.1 Purpose

6.2 Implementation Activities

6.3 Implementation Flow

7. Scope Implementation

7.1 Purpose

7.2 Scope Considerations

7.3 Scope Model

8. Leadership Implementation

8.1 Purpose

8.2 Leadership Activities

8.3 Leadership Flow

9. AI Governance Policy Implementation

9.1 Purpose

9.2 Implementation Requirements

9.3 Policy Implementation Flow

10. Governance Roles Implementation

10.1 Purpose

10.2 Role Categories

10.3 Responsibility Model

11. Competence Implementation

11.1 Purpose

11.2 Competence Areas

11.3 Competence Flow

12. Awareness Implementation

12.1 Purpose

12.2 Awareness Activities

12.3 Awareness Flow

13. Communication Implementation

13.1 Purpose

13.2 Communication Areas

13.3 Communication Flow

14. Documentation Implementation

14.1 Purpose

14.2 AIGO Documentation Layers

14.3 Documentation Control

15. AI System Registration Implementation

15.1 Purpose

15.2 Registration Activities

15.3 Registration Flow

16. AI System Classification Implementation

16.1 Purpose

16.2 Classification Factors

16.3 Classification Flow

17. AI Risk Management Implementation

17.1 Purpose

17.2 Risk Activities

17.3 Risk Flow

18. AI Impact Assessment Implementation

18.1 Purpose

18.2 Impact Areas

18.3 Impact Assessment Flow

19. Risk Treatment Implementation

19.1 Purpose

19.2 Treatment Options

19.3 Treatment Flow

20. Control Implementation

20.1 Purpose

20.2 Control Lifecycle

20.3 Control Ownership

21. Operational Planning Implementation

21.1 Purpose

21.2 Planning Areas

21.3 Planning Model

22. AI Lifecycle Implementation

22.1 Purpose

22.2 Lifecycle

22.3 Lifecycle Principle

23. Development and Acquisition Implementation

23.1 Purpose

23.2 Implementation Requirements

23.3 Acquisition Flow

24. Data Governance Implementation