AIGO — Evidence Schema Documentation

1. Document Purpose

This document describes the machine-readable AIGO Evidence Schema. The schema provides the structured representation of evidence used to support AI governance activities within the AIGO AI Governance Operating Framework. It is intended to support:

evidence identification;
evidence description;
evidence classification;
evidence sourcing;
evidence generation;
evidence collection;
evidence integrity;
evidence authenticity;
evidence completeness;
evidence relevance;
evidence accuracy;
evidence timeliness;
evidence traceability;
evidence quality;
evidence validation;
evidence review;
evidence gaps;
evidence exceptions;
evidence use;
access control;
sensitive-information handling;
retention;
repository management;
chain of custody;
disposition; and
historical traceability.

The JSON schema defines structural requirements. This documentation explains the intended meaning, governance context, validation expectations, and traceability model for evidence records.

2. Schema Information

Field	Value
Schema	AIGO Evidence Schema
Schema Identifier	`https://aigo.example/schema/evidence/v0.1`
Schema Version	`0.1`
JSON Schema Dialect	JSON Schema Draft 2020-12
Object Type	`EVIDENCE`
File	`10-AIGO-Evidence-Schema-v0.1.json`
Status	Draft
Framework	AIGO — AI Governance Operating Framework

3. Scope

The Evidence Schema applies to evidence supporting AIGO governance activities. Evidence may support:

AI system registration;
AI system classification;
risk assessment;
risk treatment;
risk acceptance;
control implementation;
control assessment;
approval;
monitoring;
incident management;
change management;
assurance;
management review;
continual improvement;
retirement;
regulatory compliance; and
contractual or organizational obligations.

Evidence may originate from internal or external sources.

4. Object Model

The primary object represented by this schema is:

EVIDENCE

An Evidence record represents an item or controlled collection of information used to support a governance claim, assessment, decision, control, or other AIGO activity. The Evidence record should remain distinct from:

the governance record it supports;
the risk record;
the control record;
the assessment record;
the approval record;
the monitoring record;
the incident record;
the change record;
the assurance record;
the management review record; and
the improvement or retirement record.

Related records should normally be connected through stable identifiers.

5. Core Identity

An Evidence record should contain stable identity information. Key fields include:

id;
objectType;
objectVersion;
schemaVersion;
evidence title;
evidence purpose;
evidence source;
evidence status; and
evidence scope where applicable.

Example:

{
  "id": "EVID-001",
  "objectType": "EVIDENCE",
  "objectVersion": "1.0",
  "schemaVersion": "0.1"
}

The evidence identifier should remain stable throughout the evidence lifecycle. A materially separate evidence item should normally receive a distinct identifier.

6. Evidence Purpose

Evidence should have a clearly stated purpose. A purpose may be to demonstrate:

control implementation;
control operation;
completion of an assessment;
approval;
monitoring;
incident response;
change implementation;
assurance activity;
management review;
regulatory compliance;
risk treatment; or
another defined governance condition.

The purpose should make clear what governance claim the evidence is intended to support.

7. Evidence Title and Description

The evidence title should be concise and descriptive. The evidence description should provide sufficient context to explain:

what the evidence represents;
what activity generated it;
when it was generated;
what system or process it relates to;
why it is relevant; and
any important limitations.

Evidence descriptions should not contain unnecessary personal or sensitive information.

8. Evidence Types

Evidence may take many forms. Typical categories include:

DOCUMENTARY
ELECTRONIC
SYSTEM_GENERATED
HUMAN_GENERATED
AUTOMATED
MANUAL
STRUCTURED
UNSTRUCTURED
TRANSACTIONAL
ANALYTICAL
OBSERVATIONAL
TEST_BASED
DECISION_BASED
LOG
REPORT
ASSESSMENT
APPROVAL_RECORD
MEETING_RECORD
INTERVIEW_RECORD
CONFIGURATION_RECORD
SCREENSHOT
OTHER

The authoritative enumeration defined in the JSON schema is controlling. An evidence item may have more than one characteristic in the underlying governance model where appropriate.

9. Evidence Source

The source identifies where the evidence originated. Possible sources include:

AI system;
application;
database;
model;
data pipeline;
monitoring system;
governance repository;
risk register;
control repository;
procedure repository;
approval system;
assessment system;
assurance activity;
incident system;
change-management system;
supplier;
external source;
interview;
test environment; or
another approved source.

Source information is central to authenticity and reliability.

10. Source Traceability

Where possible, the evidence should identify:

source system;
source record identifier;
source owner;
source location;
source URI;
source version; and
relevant source date.

For example:

{
  "source": {
    "sourceType": "MONITORING_SYSTEM",
    "sourceSystem": "AIGO Monitoring Platform",
    "sourceRecordId": "MON-001"
  }
}

This creates a clear trail back to the originating record.

11. Evidence Generation

Evidence may be generated:

automatically;
manually;
by a person;
by an AI system;
by an application;
through a test;
through an operational process; or
through an external organization.

The record should identify the generation method where it is material to evidence reliability. Where automation is involved, the organization should consider:

generation logic;
version;
configuration;
automation controls;
timestamps; and
human review.

12. Evidence Collection

The collection method should be recorded where it affects integrity or reliability. Collection methods may include:

direct capture;
system export;
repository retrieval;
manual upload;
API extraction;
screenshot;
report generation;
log export;
interview record; or
observation.

Collection records may identify:

collector;
date and time;
collection tool;
collection conditions; and
evidence location.

13. Evidence Period

Evidence may relate to a specific period. The record should identify:

start date;
end date;
effective date;
validity period; or
historical context.

This is particularly important for:

monitoring evidence;
control evidence;
compliance evidence;
incident evidence;
assurance evidence; and
management review inputs.

Evidence should not be used outside its relevant period without appropriate qualification.

14. Evidence Scope

Evidence scope should identify what the evidence covers. Scope may include:

AI systems;
business units;
jurisdictions;
environments;
components;
lifecycle stages;
activities;
users; or
time periods.

Material exclusions should be documented.

15. Evidence Relevance

Evidence should be relevant to the claim or decision it supports. Relevance may be rated as:

direct;
strong;
moderate;
limited; or
not relevant.

The rating should consider:

relationship to the requirement;
relationship to the assessment;
scope;
timing;
source; and
content.

Irrelevant evidence should not be used merely to increase evidence volume.

16. Evidence Authenticity

Authenticity concerns whether the evidence can reasonably be established as genuine and attributable to its claimed source. Authenticity may be established through:

source verification;
digital signatures;
trusted system generation;
controlled repositories;
audit trails;
access controls;
corroboration;
chain of custody; or
other approved methods.

Where authenticity cannot be confirmed, that limitation should be documented.

17. Evidence Integrity

Integrity concerns whether evidence has remained complete and unaltered from the point at which it was captured or declared authoritative. Integrity mechanisms may include:

hashes;
checksums;
digital signatures;
immutable storage;
version control;
audit trails;
repository controls;
write-protected storage; or
other mechanisms.

Where integrity is material, verification should be recorded.

18. Integrity Verification

The record may capture:

integrity mechanism;
algorithm;
hash or checksum value;
verification date;
verification method;
verifier;
verification result; and
rationale.

Example:

Evidence
  ↓
Hash Generated
  ↓
Stored
  ↓
Retrieved
  ↓
Hash Recalculated
  ↓
Comparison
  ↓
Integrity Confirmed

The exact technical mechanism should be proportionate to evidence significance.

19. Evidence Completeness

Completeness concerns whether all relevant information expected from the evidence item is present. Completeness may be assessed as:

complete;
substantially complete;
partially complete;
incomplete; or
not assessed.

Material missing elements should be identified. Incomplete evidence may still be usable if the limitation is explicitly considered and appropriately governed.

20. Evidence Accuracy

Accuracy concerns whether the evidence correctly represents the underlying fact or event. Accuracy may be assessed through:

source verification;
cross-checking;
reperformance;
independent data comparison;
technical validation;
corroborating evidence; or
other appropriate methods.

An evidence item may be authentic but inaccurate. The two characteristics should therefore be assessed separately.

21. Evidence Timeliness

Timeliness concerns whether the evidence is sufficiently current for the purpose for which it is being used. Evidence may be:

current;
acceptable;
outdated;
historically relevant; or
unknown.

Timeliness should be evaluated relative to:

governance requirement;
decision date;
system state;
risk;
control state;
legal requirement; and
intended use.

22. Evidence Traceability

Evidence should maintain traceability to its:

source;
activity;
decision;
requirement;
related records;
system;
owner; and
use.

The schema supports traceability to records such as:

Risk
Control
Assessment
Approval
Incident
Change
Assurance
Management Review
Improvement
Retirement

This supports end-to-end governance evidence chains.

23. Evidence Quality

The schema supports an overall evidence-quality assessment. Quality may consider:

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

A high-quality evidence record does not necessarily prove that the associated governance conclusion is correct. It provides information about the quality of the evidence itself.

24. Evidence Validation

Evidence validation may be required when:

the evidence is critical to a high-risk decision;
source reliability is uncertain;
the evidence is generated automatically;
evidence is externally supplied;
integrity is important;
legal or regulatory use is anticipated;
the evidence is contested; or
assurance requires independent confirmation.

Validation methods may include:

source verification;
reperformance;
cross-check;
independent review;
technical validation;
data validation; or
another approved method.

25. Evidence Review

Evidence may require formal review before use. The reviewer may determine whether the evidence is:

accepted;
accepted with conditions;
returned for clarification;
rejected; or
pending.

The review should identify:

reviewer;
review date;
result;
comments;
conditions; and
supporting records.

Evidence review should be proportionate to its significance.

26. Evidence Gaps

An evidence gap occurs when required evidence is:

missing;
incomplete;
inaccessible;
unreliable;
outdated; or
otherwise insufficient.

A gap should identify:

gap identifier;
missing evidence;
reason;
risk impact;
owner;
target date;
remediation;
status; and
risk acceptance where appropriate.

Evidence gaps should be visible rather than silently omitted.

27. Evidence Exceptions

An evidence exception may be used where a required evidence item cannot be produced or does not fully meet requirements. The exception should identify:

exception;
reason;
risk;
compensating controls;
owner;
authority;
duration;
review; and
status.

Evidence exceptions should not become a substitute for maintaining required governance records.

28. Evidence Use

The record should identify intended uses where useful. Potential uses include:

governance;
risk assessment;
risk treatment;
risk acceptance;
classification;
control assessment;
approval;
monitoring;
incident investigation;
change management;
assurance;
management review;
continual improvement;
retirement;
regulatory purposes; and
contractual purposes.

Use restrictions may also be identified.

29. Evidence Access

Evidence should be protected according to its sensitivity. Access considerations may include:

information classification;
authorized roles;
authorized users;
disclosure restrictions;
external disclosure;
access controls;
audit logging; and
segregation of duties.

The schema supports access metadata but does not enforce repository security.

30. Sensitive Information

Evidence may contain:

personal data;
sensitive personal data;
confidential business information;
security information;
credentials;
proprietary information;
regulated information; or
other sensitive information.

The evidence record should identify sensitive-information categories where applicable. Protection requirements should align with organizational security and privacy requirements. Sensitive evidence should not be unnecessarily duplicated.

31. Evidence Minimization

Only evidence necessary for the governance purpose should normally be collected and retained. Evidence minimization reduces:

privacy exposure;
security exposure;
storage burden;
unnecessary duplication; and
operational risk.

Where a summarized or redacted representation is sufficient, the organization should consider whether the full underlying evidence must be retained separately.

32. Evidence Retention

Evidence retention should be defined according to:

legal requirements;
regulatory requirements;
contractual requirements;
organizational policy;
audit requirements;
governance significance;
historical traceability; and
risk.

The retention record may identify:

retention period;
start date;
owner;
repository;
legal hold; and
legal-hold reference.

33. Legal Hold

Where evidence is subject to legal or regulatory preservation requirements, the organization should record the applicable legal hold. Evidence under legal hold should not be disposed of until authorized. The record may identify:

hold status;
hold reference;
responsible authority;
date established; and
release date.

34. Evidence Repository

The repository should provide appropriate:

access control;
version control;
backup;
integrity protection;
audit logging;
availability;
retention management; and
controlled disposition.

The Evidence record should identify the primary repository and owner where applicable.

35. Chain of Custody

Chain of custody should be used where evidence integrity depends on controlled handling. This is especially relevant for:

significant incidents;
regulatory matters;
legal proceedings;
security investigations;
contested evidence;
high-consequence assurance; and
forensic activities.

Custody events may include:

Created
Collected
Transferred
Reviewed
Validated
Archived
Disposed

Each material custody event should identify:

timestamp;
custodian;
action;
location;
integrity check; and
notes where necessary.

36. Evidence Reliability

Evidence reliability should consider:

source reliability;
generation process;
human involvement;
system integrity;
validation;
historical consistency; and
other contextual factors.

Reliability may be rated:

high;
moderate;
low; or
unknown.

Reliability should be assessed in context.

37. System-Generated Evidence

System-generated evidence may include:

logs;
monitoring data;
configuration records;
audit trails;
workflow records;
transaction records;
automated reports; and
machine-generated assessments.

System-generated evidence should be supported by confidence in:

system integrity;
configuration;
access controls;
timestamps;
generation logic; and
change history.

Automation can increase consistency but does not automatically establish reliability.

38. AI-Generated Evidence

AI systems may themselves generate information used as evidence. Where AI-generated information is treated as evidence, additional considerations may be necessary concerning:

source model;
model version;
prompt or input;
generation date;
system configuration;
human review;
corroboration;
hallucination risk;
provenance; and
validation.

AI-generated information should not automatically be treated as authoritative simply because it is generated by a system.

39. Human-Generated Evidence

Human-generated evidence may include:

interviews;
meeting minutes;
attestations;
observations;
expert judgments;
approvals;
manual test results; and
review records.

Human-generated evidence should identify relevant authors or roles where attribution is material.

40. External Evidence

External evidence may originate from:

suppliers;
regulators;
auditors;
independent assessors;
standards organizations;
customers;
external testing providers; or
other third parties.

External evidence should receive appropriate scrutiny concerning:

provenance;
authenticity;
relevance;
timeliness;
scope;
completeness; and
contractual or legal restrictions.

41. Evidence Relationships

An evidence item may support multiple records. For example:

EVID-001
   │
   ├── supports RISK-001
   ├── supports CTRL-003
   ├── supports ASSESS-004
   ├── supports APPROVAL-002
   └── supports ASSURE-001

This allows evidence reuse while preserving a single controlled evidence record. Evidence reuse should remain appropriate to the scope and time period of each use.

42. Evidence and Risk Management

Evidence may support:

risk identification;
risk analysis;
risk treatment;
risk acceptance;
risk monitoring; and
risk reassessment.

Evidence should be sufficiently relevant and current for the specific risk decision. Historical evidence may remain relevant where the risk or event being evaluated is historical.

43. Evidence and Controls

Control evidence should demonstrate, where applicable:

control existence;
implementation;
operating performance;
review;
exceptions;
remediation;
monitoring; and
effectiveness.

Evidence of a control’s existence is not necessarily evidence that the control operated effectively.

44. Evidence and Assessments

Assessment evidence should support:

criteria;
testing;
findings;
conclusions;
limitations; and
decisions.

The Evidence record should remain independent from the Assessment record so that evidence can be reused or independently reviewed.

45. Evidence and Approvals

Approval evidence may include:

assessment results;
approval documents;
meeting records;
decision logs;
signatures;
conditions;
completion evidence; and
authority records.

For significant approvals, evidence should establish both the basis of the decision and the authority of the decision-maker.

46. Evidence and Monitoring

Monitoring evidence may include:

measurements;
alerts;
dashboards;
trend reports;
configuration;
logs;
review records; and
monitoring decisions.

Monitoring evidence should preserve the relevant observation period.

47. Evidence and Incidents

Incident evidence may include:

logs;
affected outputs;
configuration;
communications;
system state;
investigation results;
root-cause evidence;
remediation evidence; and
recovery validation.

High-consequence incident evidence may require enhanced integrity and chain-of-custody controls.

48. Evidence and Change Management

Change evidence may include:

request;
impact assessment;
test results;
approval;
implementation records;
configuration;
deployment;
rollback;
validation;
enhanced monitoring; and
closure.

Evidence should demonstrate that the change occurred as authorized.

49. Evidence and Assurance

Assurance relies heavily on evidence. Evidence should allow an assurance reviewer to:

reproduce material conclusions where feasible;
understand the source of facts;
evaluate evidence quality;
identify limitations; and
trace findings to supporting information.

Evidence should be retained according to applicable assurance and record-retention requirements.

50. Evidence and Management Review

Management review may rely on evidence such as:

risk metrics;
control assessments;
incidents;
monitoring;
assurance;
regulatory changes;
stakeholder feedback;
improvement activity; and
governance performance.

Management review records should reference the evidence supporting significant management conclusions.

51. Evidence and Improvement

Improvement records may rely on evidence demonstrating:

the problem;
root cause;
proposed solution;
implementation;
effectiveness;
unintended consequences; and
sustainability.

Improvement closure should be supported by completion and verification evidence where required.

52. Evidence and Retirement

Retirement evidence may demonstrate:

approval;
technical shutdown;
access removal;
data disposition;
dependency closure;
supplier termination;
monitoring closure;
risk closure;
final validation; and
inventory update.

Evidence should be retained according to organizational and legal requirements even after the AI system is retired.

53. Evidence Disposition

Evidence may eventually be:

retained;
archived;
transferred;
anonymized;
securely deleted;
securely destroyed; or
otherwise disposed of.

Disposition should follow the applicable retention requirements. Where disposal occurs, the record should preserve evidence that the disposition was authorized and completed.

54. Superseded Evidence

Evidence may be superseded by a newer version. The record should preserve:

supersession status;
replacement evidence;
reason;
historical retention; and
relationship between versions.

Historical evidence should not be deleted solely because a newer version exists when historical traceability is required.

55. Evidence Versioning

The evidence record should distinguish:

evidence identifier;
evidence version;
schema version;
source version;
generation date; and
collection date.

A new evidence version should be created when the underlying controlled evidence changes in a way that affects its governance use.

56. Evidence Review Frequency

Evidence should be reviewed periodically where its continuing validity matters. Review frequency may depend on:

evidence type;
risk;
governance requirement;
regulatory obligation;
system change;
control change;
retention requirement; and
intended use.

Some evidence may be valid only for a defined period.

57. Evidence Reporting

Evidence repositories may report on:

evidence completeness;
evidence quality;
evidence gaps;
evidence exceptions;
overdue evidence;
expired evidence;
evidence linked to critical controls;
evidence linked to high risks;
evidence retention status;
evidence integrity;
evidence access; and
evidence disposition.

Reporting should distinguish missing evidence from evidence that exists but has not yet been validated.

58. Evidence Quality Metrics

Organizations may establish metrics such as:

Metric	Purpose
Evidence completeness	Coverage
Evidence-quality rating	Quality
Evidence gaps	Governance exposure
Evidence exceptions	Exception exposure
Integrity verification rate	Evidence integrity
Source-verification rate	Authenticity
Overdue evidence reviews	Timeliness
Expired evidence count	Evidence currency
Evidence retention compliance	Records governance
Broken traceability links	Auditability

Metrics should have defined owners and calculation rules.

59. Evidence Validation Requirements

A valid Evidence record should satisfy:

Structural Validation

The JSON document must validate against: 10-AIGO-Evidence-Schema-v0.1.json

Identity Validation

The evidence identifier must be unique and stable.

Source Validation

The source should be identifiable where required.

Purpose Validation

The governance purpose should be defined.

Relevance Validation

Evidence should be relevant to the claim it supports.

Integrity Validation

Integrity should be assessed where material.

Authenticity Validation

Authenticity should be established where required.

Completeness Validation

Material missing elements should be identified.

Timeliness Validation

Evidence should be current or appropriately identified as historical.

Traceability Validation

Evidence should connect to the records or requirements it supports.

Access Validation

Sensitive evidence should have appropriate access restrictions.

Retention Validation

Retention requirements should be defined where applicable.

Disposition Validation

Disposal should be controlled and documented.

60. Schema Limitations

JSON Schema cannot independently determine:

whether evidence is genuine;
whether evidence is accurate;
whether evidence is sufficient;
whether evidence is relevant to a particular governance claim;
whether a source is trustworthy;
whether retention periods are legally correct;
whether access restrictions are appropriate; or
whether evidence proves a governance conclusion.

These matters require organizational procedures, qualified judgment, repository controls, security, privacy, and assurance.

61. Relationship to Templates

The Evidence Schema corresponds primarily to: guidance/03-templates/16-AIGO-AI-Evidence-Record-Template-v0.1.md It also supports evidence generated by:

AI system registration;
classification;
risk assessment;
control assessment;
approval;
monitoring;
incident;
change management;
assurance;
management review;
improvement; and
retirement activities.

The Markdown template provides the human-readable evidence record. The JSON schema provides the machine-readable structure.

62. Relationship to Procedures

The Evidence Schema should be used with applicable AIGO procedures governing:

governance records;
risk assessment;
controls;
approval;
monitoring;
incident management;
change management;
assurance;
continuous improvement;
retirement; and
records management.

Where an organization establishes a dedicated evidence-management procedure, that procedure should define:

evidence collection;
validation;
classification;
access;
retention;
review;
disposition; and
exception handling.

63. Framework Traceability

AIGO Component	Relationship
Governance Domains	Defines evidence requirements and ownership
Governance Roles	Defines evidence responsibility and access
AI Governance Lifecycle	Defines evidence required at lifecycle stages
AI Risk Management	Provides evidence supporting risk decisions
AI Governance Controls	Provides evidence of control implementation and operation
AI Governance Maturity	Provides evidence of governance capability
AI System Profiles	Provides evidence of system identity and context
Implementation Guidance	Defines evidence implementation expectations
Operational Procedures	Establishes evidence generation and use
Templates	Provides human-readable evidence records
External Mappings	Provides external compliance and assurance evidence context

64. Evidence Traceability Model

The recommended evidence chain is:

Requirement / Governance Objective
             ↓
Governance Activity
             ↓
Record / Decision / Assessment
             ↓
Evidence
             ↓
Validation
             ↓
Review
             ↓
Use
             ↓
Retention
             ↓
Disposition

A broader governance traceability chain may be:

Requirement
   ↓
Risk
   ↓
Control
   ↓
Assessment
   ↓
Approval
   ↓
Evidence
   ↓
Assurance
   ↓
Management Review
   ↓
Improvement

65. Example Record

A conceptual Evidence record may look like:

{
  "id": "EVID-001",
  "objectType": "EVIDENCE",
  "objectVersion": "1.0",
  "schemaVersion": "0.1",
  "status": "ACTIVE",
  "evidenceTitle": "Production Control Execution Report",
  "purpose": "Demonstrate operation of the required pre-production governance control.",
  "evidenceType": "SYSTEM_GENERATED",
  "source": {
    "sourceType": "CONTROL_REPOSITORY",
    "sourceRecordId": "CTRL-001"
  },
  "controlIds": [
    "CTRL-001"
  ],
  "assessmentIds": [
    "ASSESS-010"
  ],
  "approvalIds": [
    "APPROVAL-004"
  ],
  "integrity": {
    "status": "CONFIRMED",
    "mechanism": "HASH",
    "algorithm": "SHA-256"
  },
  "quality": {
    "overallRating": "HIGH"
  }
}

The actual record must conform to the authoritative JSON schema.

66. Schema Registry Relationship

This schema is registered in: schemas/00-AIGO-Schema-Registry-v0.1.json The registry should maintain:

schema identifier;
object type;
version;
controlled path;
status;
dependencies;
referenced schemas;
owner;
documentation path; and
traceability metadata.

67. Change and Version Management

Changes to the Evidence Schema should be managed through AIGO change management. Potential impacts should be assessed against:

evidence repositories;
records management;
assessment;
risk;
control;
approval;
monitoring;
incident;
change management;
assurance;
management review;
improvement;
retirement;
retention requirements;
privacy; and
validation tooling.

Breaking changes should include migration guidance where appropriate.

68. Future Extensions

Future versions may support:

digital signatures;
standardized evidence packages;
cryptographic provenance;
automated evidence collection;
evidence lineage graphs;
machine-readable retention policies;
advanced chain-of-custody structures;
evidence confidence scoring;
data-provenance metadata;
immutable evidence registries;
automated evidence-gap detection; and
cross-framework evidence reuse.

Extensions should preserve evidence integrity, provenance, traceability, and historical records.

69. Document Control

Field	Value
Document	AIGO Evidence Schema Documentation
Version	0.1
Status	Draft
Document Identifier	`AIGO-SCHEMA-DOC-010`
Document Type	Schema Documentation
Schema	`10-AIGO-Evidence-Schema-v0.1.json`
Owner
Technical Reviewer
Governance Reviewer
Approved By
Effective Date
Next Review Date

70. Document Status

Document: AIGO — Evidence Schema Documentation Version: 0.1 Status: Draft Working Name: AIGO Full Name: AI Governance Operating Framework Document Identifier: AIGO-SCHEMA-DOC-010 Document Type: Schema Documentation This document provides the human-readable interpretation, governance context, validation expectations, and traceability guidance for the AIGO Evidence Schema. End of Document

​AIGO — Evidence Schema Documentation

​1. Document Purpose

​2. Schema Information

​3. Scope

​4. Object Model

​5. Core Identity

​6. Evidence Purpose

​7. Evidence Title and Description

​8. Evidence Types

​9. Evidence Source

​10. Source Traceability

​11. Evidence Generation

​12. Evidence Collection

​13. Evidence Period

​14. Evidence Scope

​15. Evidence Relevance

​16. Evidence Authenticity

​17. Evidence Integrity

​18. Integrity Verification

​19. Evidence Completeness

​20. Evidence Accuracy

​21. Evidence Timeliness

​22. Evidence Traceability

​23. Evidence Quality

​24. Evidence Validation

​25. Evidence Review

​26. Evidence Gaps

​27. Evidence Exceptions

​28. Evidence Use

​29. Evidence Access

​30. Sensitive Information

​31. Evidence Minimization

​32. Evidence Retention

​33. Legal Hold

​34. Evidence Repository

​35. Chain of Custody

​36. Evidence Reliability

​37. System-Generated Evidence

​38. AI-Generated Evidence

​39. Human-Generated Evidence

​40. External Evidence

​41. Evidence Relationships

​42. Evidence and Risk Management

​43. Evidence and Controls

​44. Evidence and Assessments

​45. Evidence and Approvals

​46. Evidence and Monitoring

​47. Evidence and Incidents

​48. Evidence and Change Management

​49. Evidence and Assurance

​50. Evidence and Management Review

​51. Evidence and Improvement

​52. Evidence and Retirement

​53. Evidence Disposition

​54. Superseded Evidence

​55. Evidence Versioning

​56. Evidence Review Frequency

​57. Evidence Reporting

​58. Evidence Quality Metrics

​59. Evidence Validation Requirements

​Structural Validation

​Identity Validation

​Source Validation

​Purpose Validation

​Relevance Validation

​Integrity Validation

​Authenticity Validation

​Completeness Validation

​Timeliness Validation

​Traceability Validation

​Access Validation

​Retention Validation

​Disposition Validation

​60. Schema Limitations

​61. Relationship to Templates

​62. Relationship to Procedures

​63. Framework Traceability

​64. Evidence Traceability Model

​65. Example Record

​66. Schema Registry Relationship