AIGO — NIST AI RMF 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-NIST-AIRMF-007 Mapping Standard: NIST AI RMF 1.0 Mapping Type: Evidence Mapping

1. Purpose

This document defines the relationship between the NIST AI Risk Management Framework (AI RMF) and the AIGO evidence architecture. The purpose of this mapping is to establish how AI governance and risk-management activities can be demonstrated through controlled, attributable, reviewable, and traceable evidence. The mapping connects:

NIST AI RMF Functions;
Categories and Subcategories;
AIGO governance requirements;
AIGO lifecycle activities;
AIGO controls;
AIGO procedures;
AIGO records;
evidence owners;
assurance activities;
management decisions.

2. Evidence Mapping Objectives

The objectives are to:

establish NIST-to-AIGO evidence traceability;
define evidence expectations for AI governance activities;
support auditability;
support assurance;
demonstrate control implementation;
demonstrate risk-management activities;
demonstrate governance decisions;
support incident and change traceability;
support regulatory and standards mapping;
support continual improvement.

3. Evidence as a Governance Mechanism

Evidence demonstrates that governance requirements have been implemented and operated.

Requirement
   ↓
Governance / Process
   ↓
Control
   ↓
Activity
   ↓
Record
   ↓
Evidence
   ↓
Review
   ↓
Decision

Evidence should not be treated merely as documentation. It is a mechanism for demonstrating governance effectiveness.

4. Evidence Principles

AIGO evidence should be:

attributable;
authentic;
complete;
accurate;
current;
traceable;
reviewable;
protected;
retained appropriately;
proportionate to risk.

5. Evidence Lifecycle

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

The evidence lifecycle should be governed by applicable organizational requirements.

6. Evidence and the NIST AI RMF

The NIST AI RMF consists of four primary Functions:

GOVERN;
MAP;
MEASURE;
MANAGE.

Each Function produces governance or risk-management activities that may require evidence.

7. GOVERN Evidence

The GOVERN Function establishes governance structures, policies, accountability, risk culture, and oversight. AIGO evidence should demonstrate that these governance mechanisms exist and operate.

7.1 Governance Evidence Examples

Examples include:

AI governance policy;
governance charter;
governance roles;
responsibility assignments;
committee records;
governance decisions;
risk appetite;
governance procedures;
exception records;
management review records.

7.2 Governance Evidence Chain

Governance Requirement
        ↓
Governance Rule
        ↓
Accountable Role
        ↓
Procedure
        ↓
Activity
        ↓
Record
        ↓
Evidence

8. MAP Evidence

The MAP Function establishes context, categorizes AI risks, and identifies relevant impacts. Evidence should demonstrate that the AI system and its context have been understood.

8.1 MAP Evidence Examples

Evidence may include:

AI system profile;
intended-use documentation;
stakeholder analysis;
context assessment;
risk identification;
impact assessment;
system classification;
dependency analysis;
data context;
deployment context.

9. MEASURE Evidence

The MEASURE Function evaluates AI risks using quantitative and qualitative methods. Evidence should demonstrate that measurement activities were performed and results were reviewed.

9.1 MEASURE Evidence Examples

Examples include:

evaluation plans;
test plans;
test results;
performance measurements;
validation records;
bias or fairness assessments;
robustness testing;
security testing;
privacy assessments;
monitoring results;
measurement reports.

10. MANAGE Evidence

The MANAGE Function prioritizes and responds to identified AI risks. Evidence should demonstrate that risk responses were implemented and monitored.

10.1 MANAGE Evidence Examples

Examples include:

risk treatment plans;
mitigation records;
control implementation records;
residual-risk decisions;
risk acceptance;
escalation records;
corrective actions;
incident records;
change records;
retirement decisions.

11. Evidence and AIGO Lifecycle

Evidence should be generated throughout the AIGO AI Governance Lifecycle.

AIGO Lifecycle Stage	Typical Evidence
Govern	Policies, roles, governance decisions
Identify	System profile, context records
Classify	Classification record
Assess	Risk assessment
Treat	Treatment plan
Approve	Approval record
Deploy	Deployment authorization
Operate	Operational records
Monitor	Monitoring reports
Assure	Assurance reports
Improve	Improvement records
Change	Change records
Continue	Continuation decision
Retire	Retirement record

12. Evidence Traceability Model

AI System
   ↓
Lifecycle Stage
   ↓
NIST Function
   ↓
Requirement
   ↓
Risk
   ↓
Control
   ↓
Procedure
   ↓
Activity
   ↓
Evidence
   ↓
Decision
   ↓
Accountable Role

This traceability chain should be maintained for material governance activities.

13. Evidence Classification

AIGO evidence may be classified as:

Evidence Class	Description
Governance Evidence	Demonstrates governance structures and decisions
Risk Evidence	Demonstrates risk identification and assessment
Control Evidence	Demonstrates control implementation
Operational Evidence	Demonstrates system operation
Monitoring Evidence	Demonstrates ongoing observation
Assurance Evidence	Demonstrates independent or objective review
Decision Evidence	Demonstrates authorized decisions
Improvement Evidence	Demonstrates corrective and continual improvement activity

14. Evidence Sources

Evidence may originate from:

governance systems;
AI inventories;
risk registers;
control registers;
ticketing systems;
monitoring systems;
security systems;
privacy systems;
assessment tools;
model-management systems;
document repositories;
meeting records.

15. Evidence Ownership

Each material evidence item should have an identifiable owner or responsible function. The owner should be accountable for:

evidence generation;
evidence accuracy;
evidence availability;
evidence retention;
evidence review.

16. Evidence Accountability Model

Requirement
   ↓
Responsible Role
   ↓
Activity
   ↓
Evidence Owner
   ↓
Evidence
   ↓
Reviewer
   ↓
Assurance

17. Evidence Quality

Evidence quality should be assessed according to:

relevance;
completeness;
accuracy;
authenticity;
timeliness;
traceability;
consistency.

Poor-quality evidence may reduce assurance confidence.

18. Evidence Sufficiency

Evidence should be sufficient to support the conclusion being made. Evidence should answer, where relevant:

What was required?
What was done?
Who performed it?
When was it performed?
What was the result?
What decision followed?
Who approved the decision?
What evidence supports the conclusion?

19. Evidence Completeness

Evidence completeness means that required evidence exists across the relevant governance chain.

Requirement
   ↓
Activity
   ↓
Result
   ↓
Decision
   ↓
Evidence

Missing links should be identified as governance or assurance gaps.

20. Evidence Authenticity

Evidence should be sufficiently protected to establish that it has not been improperly altered. Mechanisms may include:

access controls;
version control;
timestamps;
system-generated records;
digital signatures;
audit trails;
controlled repositories.

21. Evidence Integrity

Evidence integrity should address:

unauthorized modification;
deletion;
corruption;
duplication;
loss;
conflicting versions.

22. Evidence Traceability

Each material evidence item should be traceable to its source activity. Possible identifiers include:

AI system ID;
risk ID;
control ID;
assessment ID;
change ID;
incident ID;
approval ID;
evidence ID.

23. Evidence Identification Model

AI-001
   ↓
RISK-001
   ↓
CTRL-001
   ↓
PROC-001
   ↓
ACT-001
   ↓
EVD-001
   ↓
DEC-001

Identifiers should be implemented consistently across AIGO records.

24. Evidence Repository

The organization should maintain an appropriate repository for controlled AI governance evidence. The repository should support, where appropriate:

access control;
versioning;
retention;
search;
audit trail;
classification;
backup.

25. Evidence Retention

Evidence should be retained according to:

legal requirements;
regulatory requirements;
contractual requirements;
organizational policy;
risk;
audit requirements.

Retention periods should be formally defined where necessary.

26. Evidence Disposal

Evidence should be disposed of securely when retention requirements expire. Disposal should consider:

confidentiality;
privacy;
legal holds;
regulatory requirements;
contractual obligations.

27. Evidence and Governance Decisions

Material AI governance decisions should be supported by evidence. Examples include:

approval;
risk acceptance;
exception;
deployment;
change;
continuation;
suspension;
retirement.

28. Decision Evidence Model

Decision Request
      ↓
Assessment
      ↓
Evidence
      ↓
Authority Review
      ↓
Decision
      ↓
Decision Record
      ↓
Monitoring

29. Evidence and Risk Management

Risk-management evidence should demonstrate:

risk identification;
risk analysis;
risk evaluation;
risk treatment;
residual-risk assessment;
acceptance;
monitoring.

30. Risk Evidence Chain

Risk
 ↓
Cause
 ↓
Potential Impact
 ↓
Assessment
 ↓
Treatment
 ↓
Residual Risk
 ↓
Acceptance / Escalation
 ↓
Monitoring

31. Evidence and Controls

Controls should produce evidence demonstrating operation where appropriate. Control evidence may include:

execution records;
approvals;
test results;
monitoring outputs;
review records;
exception records.

32. Control Evidence Model

Control Objective
      ↓
Control
      ↓
Control Activity
      ↓
Evidence
      ↓
Control Assessment
      ↓
Finding
      ↓
Corrective Action

33. Evidence and Procedures

AIGO procedures define activities that should generate or consume evidence. Examples include:

registration;
classification;
risk assessment;
control assessment;
approval;
monitoring;
assurance;
change management;
incident management;
retirement.

34. Evidence and Approval

Approval evidence should identify:

item approved;
decision;
authority;
date;
conditions;
supporting assessment.

35. Approval Evidence Model

AI System
   ↓
Assessment
   ↓
Risk / Control Status
   ↓
Approval Request
   ↓
Authorized Decision
   ↓
Approval Record

36. Evidence and Change Management

Change records should provide evidence of:

requested change;
reason;
impact;
risk assessment;
testing;
approval;
implementation;
post-change review.

37. Change Evidence Chain

Change Request
      ↓
Impact Assessment
      ↓
Risk Assessment
      ↓
Testing
      ↓
Approval
      ↓
Implementation
      ↓
Post-Change Evidence

38. Evidence and Incident Management

Incident evidence may include:

incident report;
detection record;
timeline;
investigation;
impact assessment;
containment;
escalation;
corrective action;
closure.

39. Incident Evidence Chain

Incident
   ↓
Detection
   ↓
Classification
   ↓
Containment
   ↓
Investigation
   ↓
Risk Assessment
   ↓
Corrective Action
   ↓
Closure

40. Evidence and Monitoring

Monitoring evidence demonstrates ongoing governance and risk oversight. Examples include:

performance reports;
risk indicators;
alerts;
monitoring logs;
trend analysis;
threshold breaches;
review records.

41. Monitoring Evidence Chain

Monitoring Requirement
        ↓
Metric
        ↓
Observation
        ↓
Threshold
        ↓
Result
        ↓
Review
        ↓
Action

42. Evidence and Assurance

Assurance evidence demonstrates that governance and controls have been reviewed. Examples include:

assessment reports;
audit reports;
test results;
review records;
findings;
corrective-action verification.

43. Assurance Evidence Model

Governance Requirement
       ↓
Control
       ↓
Evidence
       ↓
Assurance Test
       ↓
Result
       ↓
Finding
       ↓
Corrective Action

44. Evidence and Management Review

Management review evidence should demonstrate:

review inputs;
review date;
participants;
decisions;
actions;
assigned owners;
follow-up.

45. Management Review Evidence Chain

Governance Data
      ↓
Management Review
      ↓
Decision
      ↓
Action
      ↓
Owner
      ↓
Follow-up Evidence

46. Evidence and Continual Improvement

Improvement evidence should demonstrate:

identified issue or opportunity;
root cause where appropriate;
improvement action;
implementation;
effectiveness review.

47. Improvement Evidence Model

Finding / Opportunity
       ↓
Analysis
       ↓
Improvement Action
       ↓
Implementation
       ↓
Verification
       ↓
Effectiveness
       ↓
Closure

48. Evidence and Stakeholders

Stakeholder-related evidence may include:

consultation records;
feedback;
complaints;
impact assessments;
stakeholder decisions;
communications.

49. Evidence and Organizational Context

Context evidence may include:

organizational objectives;
business processes;
regulatory environment;
stakeholder requirements;
AI portfolio;
risk environment.

50. Evidence and AI System Profiles

The AI system profile should provide a baseline record of:

system identity;
owner;
purpose;
users;
data;
model;
environment;
classification;
risk;
controls.

51. Evidence and AI Inventory

The AI inventory provides evidence of governance coverage. Inventory records should support:

system identification;
ownership;
lifecycle status;
classification;
risk;
approval.

52. Evidence and Classification

Classification evidence should demonstrate:

classification criteria;
assessment;
classification outcome;
reviewer;
approval where required.

53. Evidence and Risk Assessment

Risk assessment evidence should include:

methodology;
identified risks;
likelihood;
impact;
risk level;
treatment;
residual risk;
reviewer.

54. Evidence and Risk Acceptance

Risk acceptance evidence should demonstrate:

risk;
rationale;
residual risk;
acceptance authority;
conditions;
review date.

55. Evidence and Exceptions

Exception evidence should include:

requirement;
deviation;
rationale;
risk;
compensating controls;
approval;
expiry or review date.

56. Evidence and Human Oversight

Human oversight evidence may include:

review records;
intervention records;
overrides;
escalations;
human decisions;
approval records.

57. Evidence and Third Parties

Third-party evidence may include:

contracts;
supplier assessments;
due diligence;
service reports;
assurance reports;
security documentation;
incident notifications.

58. Evidence and Supply Chain

Supply-chain evidence may demonstrate:

dependencies;
providers;
component versions;
assessments;
security controls;
change notifications.

59. Evidence and Security

Security evidence may include:

security assessments;
vulnerability results;
access reviews;
incident records;
security monitoring;
testing.

60. Evidence and Privacy

Privacy evidence may include:

privacy assessments;
data-flow records;
processing assessments;
consent records where applicable;
privacy controls;
privacy incident records.

61. Evidence and Data Governance

Data-governance evidence may include:

data-source records;
data-quality assessments;
lineage;
access controls;
retention records;
data-use approvals.

62. Evidence and Model Governance

Model-governance evidence may include:

model specifications;
model versions;
validation results;
approval records;
performance tests;
change records.

63. Evidence and Testing

Testing evidence should identify:

test objective;
test method;
test environment;
test date;
tester;
result;
conclusion.

64. Evidence and Validation

Validation evidence should demonstrate that the AI system satisfies defined requirements within the intended context.

65. Evidence and Performance

Performance evidence may include:

defined metrics;
baseline;
measured values;
thresholds;
trends;
exceptions.

66. Evidence and Fairness

Where applicable, evidence may include:

fairness criteria;
test methodology;
results;
limitations;
mitigation;
review.

67. Evidence and Transparency

Transparency evidence may include:

system documentation;
user information;
disclosures;
decision explanations where applicable;
communication records.

68. Evidence and Explainability

Where explainability is relevant, evidence may include:

explanation methodology;
explanation outputs;
testing;
limitations;
reviewer conclusions.

69. Evidence and Robustness

Robustness evidence may include:

stress testing;
adversarial testing;
failure testing;
resilience testing;
performance under changing conditions.

70. Evidence and Reliability

Reliability evidence may include:

uptime;
failure rates;
error rates;
incident trends;
operational testing.

71. Evidence and Safety

Where safety is relevant, evidence may include:

hazard analysis;
safety testing;
safeguards;
incidents;
safety reviews.

72. Evidence and Security Resilience

Security-resilience evidence may include:

threat assessments;
attack testing;
security controls;
response tests;
recovery evidence.

73. Evidence and Privacy Risk

Privacy-risk evidence should demonstrate:

data identification;
privacy risk assessment;
controls;
monitoring;
incident response.

74. Evidence and Impact Assessment

Impact assessments should document, where relevant:

affected groups;
potential impacts;
severity;
likelihood;
mitigations;
residual impact.

75. Evidence and Risk Treatment

Risk-treatment evidence should demonstrate:

selected treatment;
rationale;
responsible owner;
implementation;
effectiveness.

76. Evidence and Residual Risk

Residual-risk evidence should identify:

original risk;
treatment;
remaining risk;
acceptance;
monitoring requirements.

77. Evidence and Escalation

Escalation evidence should demonstrate:

trigger;
issue;
risk;
escalation path;
authority;
decision;
outcome.

78. Evidence and Suspension

Where an AI system is suspended, evidence should include:

reason;
authority;
affected scope;
effective date;
conditions for resumption.

79. Evidence and Retirement

Retirement evidence should demonstrate:

retirement decision;
authority;
reason;
shutdown;
data disposition;
records retention;
closure.

80. Evidence and Continuation

Continuation evidence should demonstrate that the system remains suitable for continued operation. Evidence may include:

review;
risk status;
performance;
incidents;
control status;
approval.

81. NIST Function Evidence Matrix

NIST Function	Evidence Category	Example
GOVERN	Governance Evidence	Policy
GOVERN	Accountability Evidence	Role assignment
GOVERN	Oversight Evidence	Review record
MAP	Context Evidence	System profile
MAP	Risk Evidence	Impact assessment
MAP	Classification Evidence	Classification record
MEASURE	Testing Evidence	Test results
MEASURE	Evaluation Evidence	Assessment report
MEASURE	Monitoring Evidence	Metrics
MANAGE	Treatment Evidence	Risk treatment
MANAGE	Decision Evidence	Risk acceptance
MANAGE	Improvement Evidence	Corrective action

82. NIST GOVERN Evidence Traceability

GOVERN
  ↓
Governance Requirement
  ↓
Policy / Role / Decision
  ↓
Activity
  ↓
Record
  ↓
Evidence
  ↓
Review

83. NIST MAP Evidence Traceability

MAP
  ↓
Context
  ↓
AI System
  ↓
Stakeholders
  ↓
Risk / Impact
  ↓
Assessment
  ↓
Evidence

84. NIST MEASURE Evidence Traceability

MEASURE
  ↓
Metric
  ↓
Test / Evaluation
  ↓
Result
  ↓
Analysis
  ↓
Conclusion
  ↓
Evidence

85. NIST MANAGE Evidence Traceability

MANAGE
  ↓
Risk
  ↓
Prioritization
  ↓
Treatment
  ↓
Decision
  ↓
Monitoring
  ↓
Evidence

86. Evidence-to-AIGO Control Mapping

Evidence Type	AIGO Control Relationship
Policy approval	Governance control
Risk assessment	Risk control
Classification	Classification control
Control assessment	Control assurance
Approval record	Approval control
Monitoring report	Monitoring control
Incident record	Incident control
Change record	Change control
Assurance report	Assurance control
Retirement record	Lifecycle control

87. Evidence-to-Procedure Mapping

Evidence	Procedure
Governance record	AI Governance Procedure
Registration record	AI System Registration Procedure
Risk assessment	AI Risk Assessment Procedure
Classification record	AI Classification Procedure
Control assessment	AI Control Assessment Procedure
Approval record	AI Approval Procedure
Change record	AI Change Management Procedure
Incident record	AI Incident Management Procedure
Monitoring report	AI Monitoring Procedure
Assurance report	AI Assurance Procedure
Risk acceptance	AI Risk Acceptance Procedure
Retirement record	AI Retirement Procedure
Improvement record	Continuous Improvement Procedure

88. Evidence-to-Lifecycle Mapping

Evidence	Lifecycle Stage
Governance policy	Govern
Context assessment	Identify
Classification record	Classify
Risk assessment	Assess
Treatment plan	Treat
Approval	Approve
Deployment record	Deploy
Operational record	Operate
Monitoring report	Monitor
Assurance report	Assure
Improvement record	Improve
Change record	Change
Continuation review	Continue
Retirement record	Retire

89. Evidence Coverage Model

AIGO should seek evidence coverage across:

Governance
   +
Risk
   +
Lifecycle
   +
Controls
   +
Operations
   +
Monitoring
   +
Assurance
   +
Improvement

90. Evidence Gap Analysis

An evidence gap exists where a required governance or risk-management activity lacks sufficient evidence. Common evidence gaps include:

undocumented decisions;
missing approvals;
incomplete risk assessments;
missing control evidence;
missing monitoring records;
incomplete incident records;
unavailable assurance evidence.

91. Evidence Gap Record

Field	Description
Gap ID	Unique identifier
AI System	Related system
NIST Function	Applicable Function
AIGO Reference	Related AIGO requirement
Evidence Requirement	Required evidence
Gap	Missing or deficient evidence
Risk	Consequence
Owner	Responsible role
Action	Remediation
Target Date	Completion target
Status	Current status
Closure Evidence	Evidence of remediation

92. Evidence Adequacy Assessment

Evidence adequacy may be assessed using:

Dimension	Question
Relevance	Does it address the requirement?
Completeness	Is enough evidence available?
Accuracy	Is the evidence correct?
Authenticity	Can its origin be established?
Timeliness	Is it current?
Traceability	Can it be linked to the activity?
Integrity	Has it been protected?

93. Evidence Confidence

Assurance confidence may increase where evidence is:

direct;
independently verified;
system-generated;
consistent;
complete;
current.

94. Evidence Hierarchy

AIGO may distinguish evidence strength.

Direct Verified Evidence
        ↓
System-Generated Evidence
        ↓
Controlled Records
        ↓
Reviewed Documentation
        ↓
Management Statements
        ↓
Unverified Statements

The appropriate evidence level depends on the assurance objective.

95. Evidence Sampling

Assurance activities may use sampling where full-population review is impractical. Sampling should consider:

risk;
population size;
materiality;
assurance objective;
confidence requirements.

96. Evidence Review

Evidence review should verify:

relevance;
completeness;
consistency;
authenticity;
applicability;
traceability.

97. Evidence Review Frequency

Evidence should be reviewed according to:

risk;
control frequency;
lifecycle stage;
regulatory requirements;
assurance requirements.

98. Evidence Exceptions

Evidence exceptions should be documented where required evidence:

cannot be produced;
is incomplete;
is unavailable;
is inaccurate;
is overdue.

Exceptions should be risk-assessed and managed.

99. Evidence Escalation

Material evidence deficiencies should be escalated when they could affect:

compliance;
risk decisions;
safety;
security;
privacy;
governance effectiveness;
assurance conclusions.

100. Evidence and Auditability

AIGO evidence architecture should support an auditor or assessor in reconstructing:

Requirement
   ↓
Decision
   ↓
Activity
   ↓
Control
   ↓
Evidence
   ↓
Result
   ↓
Corrective Action

101. Evidence and Audit Trail

Audit trails should provide sufficient information to understand:

who;
what;
when;
why;
result;
subsequent action.

102. Evidence and Version Control

Controlled evidence should identify relevant versions where applicable. Version control is particularly important for:

policies;
procedures;
model versions;
assessments;
approvals;
controls;
risk decisions.

103. Evidence and Change History

Material changes should preserve sufficient historical information to establish what changed and when.

104. Evidence and Record Retention

Records should remain accessible for the required retention period, subject to applicable legal and organizational requirements.

105. Evidence and Confidentiality

Evidence repositories should protect confidential information. Access should be limited according to:

role;
business need;
sensitivity;
legal requirements.

106. Evidence and Personal Data

Evidence containing personal data should be handled according to applicable privacy requirements. Evidence collection should avoid unnecessary personal information.

107. Evidence and Security

Evidence repositories should be protected against unauthorized access, modification, destruction, or disclosure.

108. Evidence and Third-Party Records

Third-party records should be:

identified;
assessed;
retained;
linked to relevant systems;
reviewed where necessary.

109. Evidence and Supplier Assurance

Supplier evidence may support evaluation of:

security;
reliability;
privacy;
AI risk;
service performance;
control effectiveness.

110. Evidence and Governance Reporting

Evidence should support governance reporting. Reports may summarize:

AI portfolio;
risk;
controls;
incidents;
monitoring;
assurance;
improvement.

111. Evidence Dashboard

Evidence Coverage
       ↓
Missing Evidence
       ↓
Evidence Quality
       ↓
Open Exceptions
       ↓
Assurance Findings
       ↓
Corrective Actions

112. Evidence Metrics

Potential evidence metrics include:

percentage of required evidence available;
percentage of evidence current;
evidence exception count;
overdue evidence count;
evidence-quality findings;
evidence retrieval time;
evidence-related audit findings.

113. Evidence Maturity

AIGO evidence maturity may progress through:

Level	Evidence Capability
1	Ad hoc
2	Documented
3	Controlled
4	Integrated
5	Optimized

114. Evidence Maturity Characteristics

Level 1 — Ad Hoc

Evidence is inconsistent and largely reactive.

Level 2 — Documented

Evidence expectations are documented.

Level 3 — Controlled

Evidence is governed through defined ownership and retention.

Level 4 — Integrated

Evidence is connected across governance, risk, controls, lifecycle, and assurance.

Level 5 — Optimized

Evidence is highly automated, measurable, traceable, and continuously improved.

115. Evidence Automation

Where appropriate, evidence generation may be automated. Examples include:

system logs;
monitoring outputs;
approval workflows;
control execution records;
security alerts;
audit trails.

Automation should not compromise evidence integrity.

116. Evidence and AI Monitoring

Monitoring systems can generate continuous evidence. Examples include:

performance metrics;
drift indicators;
availability;
incidents;
threshold breaches.

117. Evidence and Model Drift

Where model drift is relevant, evidence may include:

drift measurements;
thresholds;
alerts;
investigation;
remediation;
approval.

118. Evidence and Data Drift

Where data drift is relevant, evidence may include:

baseline;
observed distribution;
drift metrics;
threshold;
review;
action.

119. Evidence and Incident Trends

Incident evidence may be aggregated to identify:

recurring failures;
systemic risks;
control weaknesses;
emerging issues.

120. Evidence and Corrective Actions

Corrective-action evidence should demonstrate:

issue;
root cause;
action;
owner;
completion;
effectiveness.

121. Evidence Closure

An evidence-related finding should not be considered closed solely because an action was performed. Closure should be supported by appropriate verification.

122. Evidence Effectiveness

Effectiveness review should determine whether the implemented action resolved the identified deficiency.

123. Evidence Continual Improvement

Evidence findings should feed the continual improvement process.

Evidence Review
      ↓
Finding
      ↓
Root Cause
      ↓
Improvement
      ↓
Verification
      ↓
Updated Evidence

124. Evidence Interoperability

Evidence should be capable of linking across AIGO records. Relevant links include:

AI system;
risk;
control;
procedure;
incident;
change;
approval;
evidence;
assurance.

125. Evidence Relationship Model

AI System
   ↕
Risk
   ↕
Control
   ↕
Procedure
   ↕
Evidence
   ↕
Decision
   ↕
Assurance

126. Evidence Mapping Matrix

AIGO Object	Evidence
AI System	System Profile
Risk	Risk Assessment
Control	Control Assessment
Procedure	Procedure Record
Approval	Approval Record
Incident	Incident Record
Change	Change Record
Monitoring	Monitoring Report
Assurance	Assurance Report
Improvement	Improvement Record
Retirement	Retirement Record

127. NIST AI RMF Evidence Coverage

The AIGO evidence architecture supports evidence for all four NIST AI RMF Functions:

Function	Evidence Coverage
GOVERN	Governance and accountability
MAP	Context and risk
MEASURE	Evaluation and measurement
MANAGE	Treatment and response

128. End-to-End NIST Evidence Traceability

NIST AI RMF
      ↓
Function
      ↓
Category / Subcategory
      ↓
AIGO Requirement
      ↓
AIGO Lifecycle
      ↓
AIGO Control
      ↓
AIGO Procedure
      ↓
Governance Activity
      ↓
Evidence
      ↓
Assurance
      ↓
Decision

129. Evidence Gap-to-Improvement Cycle

Evidence Gap
      ↓
Risk Assessment
      ↓
Corrective Action
      ↓
Implementation
      ↓
Evidence Generation
      ↓
Verification
      ↓
Closure
      ↓
Continual Improvement

130. Evidence Governance Model

The AIGO evidence model should establish:

evidence requirements;
evidence owners;
evidence sources;
evidence repositories;
evidence quality;
retention;
access;
assurance;
improvement.

131. Evidence Governance Responsibilities

Responsibility	Role
Define evidence requirement	Control / Governance Owner
Generate evidence	Process / Control Owner
Maintain evidence	Evidence Owner
Review evidence	Reviewer
Assess evidence	Assessor
Assure evidence	Assurance Function
Approve decisions	Authorized Governance Role
Improve evidence process	Governance / Improvement Owner

132. Evidence Control Requirements

Evidence controls should address:

identification;
ownership;
access;
integrity;
version control;
retention;
retrieval;
review;
disposal.

133. Evidence Control Model

Evidence Requirement
        ↓
Evidence Creation
        ↓
Evidence Control
        ↓
Evidence Review
        ↓
Evidence Retention
        ↓
Assurance

134. Evidence Mapping Limitations

This mapping:

does not reproduce NIST AI RMF;
does not constitute NIST certification;
does not constitute NIST endorsement;
does not establish legal compliance;
does not replace organization-specific evidence requirements;
does not replace technical testing;
does not replace assurance judgment.

It provides an AIGO evidence traceability model aligned to NIST AI RMF concepts.

135. Maintenance Requirements

This document should be reviewed when:

NIST AI RMF changes;
AIGO evidence architecture changes;
AIGO controls change;
procedures change;
lifecycle requirements change;
governance requirements change;
applicable regulations change;
assurance findings identify evidence gaps;
organizational context materially changes.

136. Document Change Record

Version	Date	Change	Author	Reviewer	Approval
0.1		Initial NIST AI RMF Evidence Mapping

137. Document Control

137.1 Controlled Information

Field	Value
Document Title	AIGO — NIST AI RMF Evidence Mapping
Document ID	`AIGO-MAP-NIST-AIRMF-007`
Version	0.1
Status	Draft
Framework	AIGO AI Governance Operating Framework
Mapping Standard	NIST AI RMF 1.0
Mapping Domain	Evidence Management
Primary Owner
Technical Reviewer
Governance Reviewer
Approver
Effective Date
Next Review Date

138. Final Control Statement

This document establishes the evidence-level relationship between the NIST AI Risk Management Framework and the AIGO AI Governance Operating Framework. It provides a structured model for demonstrating:

governance;
organizational context;
AI risk identification;
risk assessment;
measurement;
risk treatment;
control operation;
approvals;
monitoring;
incidents;
changes;
assurance;
continual improvement.

Evidence should remain attributable, traceable, protected, reviewable, and proportionate to the risk and governance significance of the underlying activity. This document should be maintained as a controlled living document and updated whenever the underlying NIST framework, AIGO architecture, governance requirements, controls, procedures, lifecycle, evidence requirements, or organizational context materially changes.

139. End of Mapping Document

AIGO — NIST AI RMF Evidence Mapping Document ID: AIGO-MAP-NIST-AIRMF-007 Version: 0.1 Status: Draft Mapping Standard: NIST AI RMF 1.0 Mapping Type: Evidence Mapping End of Document

​AIGO — NIST AI RMF Evidence Mapping

​AIGO — AI Governance Operating Framework

​1. Purpose

​2. Evidence Mapping Objectives

​3. Evidence as a Governance Mechanism

​4. Evidence Principles

​5. Evidence Lifecycle

​6. Evidence and the NIST AI RMF

​7. GOVERN Evidence

​7.1 Governance Evidence Examples

​7.2 Governance Evidence Chain

​8. MAP Evidence

​8.1 MAP Evidence Examples

​9. MEASURE Evidence

​9.1 MEASURE Evidence Examples

​10. MANAGE Evidence

​10.1 MANAGE Evidence Examples

​11. Evidence and AIGO Lifecycle

​12. Evidence Traceability Model

​13. Evidence Classification

​14. Evidence Sources

​15. Evidence Ownership

​16. Evidence Accountability Model

​17. Evidence Quality

​18. Evidence Sufficiency

​19. Evidence Completeness

​20. Evidence Authenticity

​21. Evidence Integrity

​22. Evidence Traceability

​23. Evidence Identification Model

​24. Evidence Repository

​25. Evidence Retention

​26. Evidence Disposal

​27. Evidence and Governance Decisions

​28. Decision Evidence Model

​29. Evidence and Risk Management

​30. Risk Evidence Chain

​31. Evidence and Controls

​32. Control Evidence Model

​33. Evidence and Procedures

​34. Evidence and Approval

​35. Approval Evidence Model

​36. Evidence and Change Management

​37. Change Evidence Chain

​38. Evidence and Incident Management

​39. Incident Evidence Chain

​40. Evidence and Monitoring

​41. Monitoring Evidence Chain

​42. Evidence and Assurance

​43. Assurance Evidence Model

​44. Evidence and Management Review

​45. Management Review Evidence Chain

​46. Evidence and Continual Improvement

​47. Improvement Evidence Model

​48. Evidence and Stakeholders

​49. Evidence and Organizational Context

​50. Evidence and AI System Profiles

​51. Evidence and AI Inventory

​52. Evidence and Classification

​53. Evidence and Risk Assessment

​54. Evidence and Risk Acceptance

​55. Evidence and Exceptions

​56. Evidence and Human Oversight

​57. Evidence and Third Parties

​58. Evidence and Supply Chain

​59. Evidence and Security

​60. Evidence and Privacy

​61. Evidence and Data Governance

​62. Evidence and Model Governance

​63. Evidence and Testing

​64. Evidence and Validation

​65. Evidence and Performance

​66. Evidence and Fairness

​67. Evidence and Transparency

​68. Evidence and Explainability

​69. Evidence and Robustness

​70. Evidence and Reliability

​71. Evidence and Safety

​72. Evidence and Security Resilience

​73. Evidence and Privacy Risk

AIGO — NIST AI RMF Evidence Mapping

AIGO — AI Governance Operating Framework

1. Purpose

2. Evidence Mapping Objectives

3. Evidence as a Governance Mechanism

4. Evidence Principles

5. Evidence Lifecycle

6. Evidence and the NIST AI RMF

7. GOVERN Evidence

7.1 Governance Evidence Examples

7.2 Governance Evidence Chain

8. MAP Evidence

8.1 MAP Evidence Examples

9. MEASURE Evidence

9.1 MEASURE Evidence Examples

10. MANAGE Evidence

10.1 MANAGE Evidence Examples

11. Evidence and AIGO Lifecycle

12. Evidence Traceability Model

13. Evidence Classification

14. Evidence Sources

15. Evidence Ownership

16. Evidence Accountability Model

17. Evidence Quality

18. Evidence Sufficiency

19. Evidence Completeness

20. Evidence Authenticity

21. Evidence Integrity

22. Evidence Traceability

23. Evidence Identification Model

24. Evidence Repository

25. Evidence Retention

26. Evidence Disposal

27. Evidence and Governance Decisions

28. Decision Evidence Model

29. Evidence and Risk Management

30. Risk Evidence Chain

31. Evidence and Controls

32. Control Evidence Model

33. Evidence and Procedures

34. Evidence and Approval

35. Approval Evidence Model

36. Evidence and Change Management

37. Change Evidence Chain

38. Evidence and Incident Management

39. Incident Evidence Chain

40. Evidence and Monitoring

41. Monitoring Evidence Chain

42. Evidence and Assurance

43. Assurance Evidence Model

44. Evidence and Management Review

45. Management Review Evidence Chain

46. Evidence and Continual Improvement

47. Improvement Evidence Model

48. Evidence and Stakeholders

49. Evidence and Organizational Context

50. Evidence and AI System Profiles

51. Evidence and AI Inventory

52. Evidence and Classification

53. Evidence and Risk Assessment

54. Evidence and Risk Acceptance

55. Evidence and Exceptions

56. Evidence and Human Oversight

57. Evidence and Third Parties

58. Evidence and Supply Chain

59. Evidence and Security

60. Evidence and Privacy

61. Evidence and Data Governance

62. Evidence and Model Governance

63. Evidence and Testing

64. Evidence and Validation

65. Evidence and Performance

66. Evidence and Fairness

67. Evidence and Transparency

68. Evidence and Explainability

69. Evidence and Robustness

70. Evidence and Reliability

71. Evidence and Safety

72. Evidence and Security Resilience

73. Evidence and Privacy Risk