> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aigoframework.com/llms.txt
> Use this file to discover all available pages before exploring further.

# 07 AIGO NIST AI RMF Evidence Mapping v0.1

# 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:

1. establish NIST-to-AIGO evidence traceability;
2. define evidence expectations for AI governance activities;
3. support auditability;
4. support assurance;
5. demonstrate control implementation;
6. demonstrate risk-management activities;
7. demonstrate governance decisions;
8. support incident and change traceability;
9. support regulatory and standards mapping;
10. support continual improvement.

***

## 3. Evidence as a Governance Mechanism

Evidence demonstrates that governance requirements have been implemented and operated.

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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.

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
GOVERN
  ↓
Governance Requirement
  ↓
Policy / Role / Decision
  ↓
Activity
  ↓
Record
  ↓
Evidence
  ↓
Review
```

***

# 83. NIST MAP Evidence Traceability

```text theme={null}
MAP
  ↓
Context
  ↓
AI System
  ↓
Stakeholders
  ↓
Risk / Impact
  ↓
Assessment
  ↓
Evidence
```

***

# 84. NIST MEASURE Evidence Traceability

```text theme={null}
MEASURE
  ↓
Metric
  ↓
Test / Evaluation
  ↓
Result
  ↓
Analysis
  ↓
Conclusion
  ↓
Evidence
```

***

# 85. NIST MANAGE Evidence Traceability

```text theme={null}
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:

```text theme={null}
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.

```text theme={null}
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:

```text theme={null}
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

```text theme={null}
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.

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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

```text theme={null}
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:

1. identification;
2. ownership;
3. access;
4. integrity;
5. version control;
6. retention;
7. retrieval;
8. review;
9. disposal.

***

# 133. Evidence Control Model

```text theme={null}
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**