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TG-TMS

Trust Model Specification

1. Purpose

The TrustGate Trust Model Specification (TG-TMS) defines the canonical trust model for the ZAYAZ platform.

It establishes the principles, terminology, dimensions, mathematical foundations, and governance rules used to determine trustworthiness across every computational artifact, runtime signal, assurance workflow, and federated exchange.

Rather than prescribing implementation details, this specification defines the conceptual model that all TrustGate components shall implement consistently.

The Trust Model therefore serves as the normative reference for:

TrustGate runtime engines;
replay verification;
digital attestations;
assurance workflows;
federation interoperability;
AI-assisted reasoning;
trust scoring;
governance analytics;
audit evidence.

Runtime observability is captured through canonical TrustGate Telemetry Events (trustgate_telemetry_event) which provide operational evidence supporting replay, observability, AI learning and Trust Intelligence.

2. Scope

This specification applies to every artifact participating in trust evaluation, including:

runtime signals;
canonical metrics;
validation results;
replay artifacts;
trust attestations;
ledger anchors;
AI-generated observations;
federation exchanges;
verification workflows;
governance decisions.

The specification is intentionally platform-wide.

Although implemented primarily through TrustGate, its trust concepts are shared across ZAYAZ.

3. Vision

Trust within ZAYAZ is not represented as a binary state.

An artifact is never simply "trusted" or "not trusted."

Instead, trust is modeled as a multidimensional representation of confidence derived from evidence, lineage, validation, provenance, governance, and computational integrity.

This approach enables:

explainable assurance;
deterministic replay;
transparent AI reasoning;
federated interoperability;
long-term governance.

4. Design Principles

The Trust Model is governed by the following principles.

4.1. Evidence Before Trust

Trust shall always be derived from verifiable evidence.

Assertions without supporting evidence shall never increase trust.

3## 4.2. Explainability

Every trust decision shall be explainable.

It shall always be possible to determine:

why a score was assigned;
which evidence contributed;
which validation rules executed;
which engines participated;
which assumptions were applied.

4.3. Deterministic Reproducibility

Given identical inputs, TrustGate shall always reproduce identical trust outcomes.

Replay must therefore produce the same trust vector as the original execution, unless explicitly re-evaluated under a different policy or model version.

4.4. Complete Lineage

Every trust decision shall preserve complete computational lineage through USO, CSI, CMID, MEID, CMI, and DAL.

No trust decision shall exist without traceability.

4.5. Federation Neutrality

Trust semantics shall remain identical across organizations participating in federated assurance.

Only evidence differs—not the underlying meaning of trust.

4.6. Human Oversight

TrustGate augments human assurance.

It does not replace accountable decision makers.

Human reviewers may approve, reject, override, or annotate trust outcomes while preserving complete audit history.

5. Relationship to TrustGate

TrustGate is the operational implementation of this Trust Model.

The Trust Model defines:

trust semantics;
confidence principles;
evidence requirements;
governance concepts;
trust dimensions.

TrustGate engines implement these principles through executable logic.

6. Relationship to Other Specifications

This specification forms the conceptual foundation for:

Specification	Relationship
TrustGate MIB	Runtime architecture implementing the Trust Model
TrustGate Micro-Engine Catalog	Executable engines implementing trust functions
TrustGate Validation Rule Registry	Evidence generation through validation
TrustGate CSI Catalog	Canonical trust signal structures
TrustGate Signal Catalog	Governance of trust-related signals
Replay Specification	Deterministic reconstruction of trust
Federation Profiles	Cross-organization trust interoperability
Trust Scoring Rubric	Mathematical implementation of trust computation
DAL Specification	Immutable preservation of trust evidence
DSAIL Learning Specification	AI learning from trust outcomes
EGFS Federation Specification	Global governance of federated trust

7. Canonical Trust Architecture

Evidence
    │
    ▼
Validation
    │
    ▼
Trust Dimensions
    │
    ▼
Trust Vector
    │
    ▼
Trust Decision
    │
    ▼
Attestation
    │
    ▼
DAL Anchor
    │
    ▼
Federation
    │
    ▼
AI Learning

Every stage enriches the trust context while preserving complete computational lineage.

8. Trust as a Vector

TrustGate represents trust as a multidimensional Trust Vector rather than a single scalar score.

Each dimension evaluates a distinct aspect of trustworthiness.

This approach provides greater transparency, supports explainable AI, and enables auditors to understand precisely why an artifact is or is not trusted.

The canonical Trust Vector is defined in Part 3 of this specification.

9. Conformance

Every TrustGate engine that generates, modifies, evaluates, or consumes trust information shall conform to this specification.

Compliance requires:

use of canonical terminology;
preservation of lineage;
deterministic processing;
explainable outcomes;
reproducible trust evaluation;
compatibility with replay;
compatibility with federation;
support for Trust Vectors.

10. References

TrustGate MIB
TrustGate Micro-Engine Catalog
TrustGate Validation Rule Registry
TrustGate CSI Catalog
TrustGate Signal Catalog
TrustGate Replay Specification
TrustGate Federation Profiles
TrustGate Scoring Rubric
DAL Specification
DSAIL Learning Specification
EGFS Federation Specification
Universal Signal Ontology (USO)
Canonical Metric Identifier (CMID)
Canonical Signal Identifier (CSI)
ZAYAZ Artifact Registry (ZAR)

11. Introduction

The Canonical Trust Model defines the semantic foundation upon which every TrustGate assurance decision is based.

Rather than representing trust as a single scalar value, TrustGate models trust as a structured computational object derived from verifiable evidence, deterministic processing, computational lineage, and governance.

The Canonical Trust Model therefore establishes a common language that enables every TrustGate engine to reason about trust consistently.

It defines:

what trust is;
what contributes to trust;
how trust evolves;
how trust is preserved;
how trust is exchanged;
how trust can be reproduced.

This model is normative for every TrustGate implementation.

12. Trust as a Computational Object

Within ZAYAZ, trust is treated as a first-class computational artifact.

Trust is neither subjective nor binary.

Instead, trust is an emergent property resulting from observable evidence processed through deterministic assurance logic.

A Trust Object consists of:

identity;
evidence;
validation history;
provenance;
computational lineage;
confidence;
uncertainty;
governance metadata;
lifecycle state;
Trust Vector.

Every Trust Object is uniquely associated with one or more runtime artifacts through USO lineage.

13. Canonical Definition of Trust

Trust is defined as:

The measurable degree of justified confidence that a computational artifact faithfully represents reality, based on verifiable evidence, deterministic processing, complete lineage, and governed assurance.

This definition intentionally excludes assumptions, opinions, and unverifiable assertions.

Trust is therefore always evidence-based.

14. Canonical Trust Components

Trust is composed of multiple canonical components.

14.1. Evidence

Evidence represents all information supporting a trust assessment.

Examples include:

measurements;
documents;
invoices;
telemetry;
digital signatures;
validation outcomes;
replay verification;
verifier attestations.

Evidence never disappears.

New evidence extends existing trust.

14.2. Provenance

Provenance describes where evidence originated.

It includes:

producer identity;
originating organization;
acquisition method;
acquisition timestamp;
acquisition confidence.

14.3. Lineage

Lineage represents the complete computational history of an artifact.

TrustGate records lineage through:

USO Instance;
CSI;
CMID;
CMI;
MEID;
DAL references.

Complete lineage enables deterministic replay.

14.4. Integrity

Integrity measures whether an artifact has remained unmodified since its creation.

Integrity may be demonstrated through:

cryptographic hashes;
digital signatures;
DAL anchors;
immutable storage;
replay verification.

14.5. Authenticity

Authenticity measures whether an artifact genuinely originates from its declared source.

Typical mechanisms include:

DID verification;
verifier credentials;
digital certificates;
federation identity;
cryptographic proofs.

14.6. Consistency

Consistency measures semantic agreement across related evidence.

Examples include:

identical totals across reports;
accounting reconciliation;
energy balances;
mass balances;
emissions consistency.

Consistency increases trust.

Conflicts reduce trust.

14.7. Completeness

Completeness evaluates whether all expected evidence has been supplied.

Examples:

missing invoices;
incomplete Scope 3 categories;
absent signatures;
omitted facilities;
missing reporting periods.

14.8. Timeliness

Trust depends upon temporal validity.

Evidence may:

expire;
become obsolete;
require replay;
require revalidation.

Older evidence may therefore contribute less confidence than newer evidence.

14.9. Governance

Governance represents organizational oversight.

Examples include:

approval workflows;
segregation of duties;
reviewer independence;
verifier accreditation;
policy compliance.

Governance provides institutional confidence.

15. Trust Ontology

Every Trust Object is modeled using the following ontology.

Trust Object
│
├── Identity
│
├── Evidence
│
├── Provenance
│
├── Lineage
│
├── Integrity
│
├── Authenticity
│
├── Consistency
│
├── Completeness
│
├── Timeliness
│
├── Governance
│
├── Confidence
│
├── Uncertainty
│
└── Trust Vector

Every node may itself reference one or more supporting computational artifacts.

16. Trust Relationships

TrustGate distinguishes between multiple trust relationships.

Direct Trust

Derived directly from observed evidence.

Examples:

verified invoice;
signed measurement;
digitally authenticated report.

Derived Trust

Inherited through deterministic computation.

Examples:

calculated KPI;
emissions aggregation;
benchmark comparison.

Federated Trust

Transferred from external assurance providers.

Examples:

accredited verifier;
government registry;
federation partner.

Replay Trust

Established by deterministic reconstruction.

Replay demonstrates that a historical execution can be reproduced.

AI Trust

Derived from AI-assisted observations.

AI never creates trust independently.

Instead, AI contributes evidence subject to validation.

17. Trust Lifecycle

Trust evolves throughout the lifetime of an artifact.

Observed

↓

Validated

↓

Verified

↓

Trusted

↓

Attested

↓

Anchored

↓

Federated

↓

Archived

↓

Expired

↓

Revalidated

Trust is therefore dynamic rather than static.

18. Trust Invariants

The following properties shall always remain true.

TI-001

Trust cannot exist without evidence.

TI-002

Trust shall always preserve lineage.

TI-003

Trust shall always be reproducible through replay.

TI-004

Trust shall always be explainable.

TI-005

Trust shall never increase through undocumented assumptions.

TI-006

Trust shall remain computationally deterministic.

TI-007

Trust decisions shall preserve complete auditability.

TI-008

Trust may decrease as evidence ages or becomes invalid.

TI-009

Additional independent evidence shall never reduce trust unless contradictory.

TI-010

Every Trust Object shall be uniquely identifiable and referenceable.

19. Relationship to Canonical Artifacts

The Trust Model binds together the principal canonical artifacts of the ZAYAZ platform.

Artifact	Role in Trust Model
CMID	Identifies the canonical metric to which trust ultimately applies.
CSI	Defines the canonical signal contract carrying trust-relevant data.
USO Type	Defines the semantic classification of the trust signal.
USO Instance	Represents the runtime occurrence ("signal birth") from which trust lineage begins.
MEID	Identifies the canonical micro-engine responsible for producing, transforming, validating, or enriching evidence.
CMI	Identifies the executable implementation that fulfilled the MEID during runtime.
TG-VAL	Records the validation rules executed during trust evaluation.
Trust Vector	Captures the multidimensional assessment produced from the accumulated evidence.
Trust Decision	Represents the governed outcome derived from the Trust Vector.
Trust Attestation	A signed assurance artifact encapsulating the trust decision and supporting metadata.
DAL Anchor	Provides immutable cryptographic anchoring and long-term preservation of trust evidence.

Collectively these artifacts create an end-to-end, replayable, explainable, and federatable trust chain spanning every computational stage within the ZAYAZ platform.

20. Transition to the Trust Dimensions

The Canonical Trust Model defines the conceptual building blocks of trust.

The following chapters introduces the Canonical Trust Vector, which operationalizes these concepts into measurable trust dimensions used by TrustGate engines, AI systems, replay verification, federation exchanges, and digital attestations.

Each Trust Vector dimension evaluates one aspect of the Trust Object while preserving complete traceability back to the evidence, validation rules, computational lineage, and governance processes defined in this specification.

21. Purpose

The Canonical Trust Vector (CTV) is the primary representation of trust within the ZAYAZ platform.

Rather than reducing trust to a single percentage, the Trust Vector expresses trust as a collection of independently evaluated dimensions.

Each dimension measures a different property of trustworthiness while remaining fully traceable to evidence, validation rules, runtime processing, and governance decisions.

The Trust Vector is therefore the canonical output of the Trust Object.

22. Trust Vector Architecture

                     Trust Object (TO)
                             │
        ┌────────────────────┼────────────────────┐
        │                    │                    │
   Evidence            Computational         Governance
        │                 Lineage              Context
        └────────────────────┼────────────────────┘
                             │
                             ▼
                 Canonical Trust Vector (CTV)
                             │
      ┌──────────────┬──────────────┬──────────────┐
      ▼              ▼              ▼
 Structural      Semantic      Computational
      ▼              ▼              ▼
 Cryptographic   Operational     Replay
      ▼              ▼              ▼
 Federation          AI        Governance
      ▼
 Temporal
                             │
                             ▼
                  Trust Decision & Attestation

The Trust Vector is immutable for a given evaluation event and becomes part of the Trust Object's permanent lineage.

23. Canonical Trust Dimensions

The initial version of the Canonical Trust Vector consists of ten orthogonal dimensions.

Code	Dimension	Purpose
ST	Structural Trust	Structural correctness of artifacts
SM	Semantic Trust	Semantic validity and consistency
CT	Computational Trust	Confidence in computational execution
CR	Cryptographic Trust	Integrity and authenticity guarantees
OP	Operational Trust	Runtime execution quality
RP	Replay Trust	Ability to deterministically reproduce results
FD	Federation Trust	Confidence derived from federation participants
AI	AI Trust	Confidence associated with AI-assisted reasoning
GV	Governance Trust	Oversight, approval, policy and assurance quality
TM	Temporal Trust	Freshness and temporal validity

Additional dimensions may be introduced through future revisions without changing the conceptual model.

24. Structural Trust

Structural Trust measures whether information conforms to its expected structural definition.

Typical contributors include:

CSI validation;
JSON Schema validation;
datatype validation;
required field validation;
cardinality validation;
canonical identifier validation;
schema version compatibility.

Structural Trust answers:

"Is the artifact structurally valid?"

25. Semantic Trust

Semantic Trust evaluates whether information is logically meaningful.

Examples include:

unit compatibility;
business-rule validation;
accounting consistency;
environmental mass balance;
ontology compliance;
cross-metric reconciliation;
canonical taxonomy alignment.

Semantic Trust answers:

"Does the information make sense?"

26. Computational Trust

Computational Trust measures confidence in the execution process itself.

It considers:

deterministic execution;
verified MEIDs;
approved CMIs;
runtime determinism;
execution reproducibility;
computation integrity;
approved algorithms.

Computational Trust answers:

"Can the computation itself be trusted?"

27. Cryptographic Trust

Cryptographic Trust evaluates mathematical guarantees protecting artifacts.

Evidence may include:

digital signatures;
DID verification;
hash verification;
DAL anchors;
certificate validation;
immutable storage proofs.

Cryptographic Trust answers:

"Can this artifact be proven authentic and unmodified?"

28. Operational Trust

Operational Trust measures runtime execution quality.

Factors include:

runtime health;
execution success;
engine availability;
timeout handling;
retry behaviour;
orchestration quality;
runtime observability.

Operational Trust answers:

"Did the platform execute reliably?"

29. Replay Trust

Replay Trust measures reproducibility.

Evaluation includes:

replay fidelity;
deterministic reconstruction;
historical equivalence;
lineage completeness;
replay checksum verification;
identical Trust Vector reproduction.

Replay Trust answers:

"Can this result be reproduced?"

30. Federation Trust

Federation Trust evaluates confidence derived from external organizations.

Inputs include:

verifier reputation;
federation profile;
accreditation;
cross-certification;
partner reliability;
federation policy compliance.

Federation Trust answers:

"How much can external contributors be trusted?"

31. AI Trust

AI Trust measures the contribution of artificial intelligence to an assessment.

It considers:

model confidence;
hallucination detection;
explanation quality;
corroboration by evidence;
human review;
verifier overrides;
historical model performance.

AI Trust answers:

"How much confidence should be placed in AI-assisted reasoning?"

Importantly, AI Trust can contribute to—but never independently determine—overall trust.

32. Governance Trust

Governance Trust evaluates organizational oversight.

Examples include:

reviewer independence;
approval workflows;
segregation of duties;
policy compliance;
verifier accreditation;
audit quality;
regulatory alignment.

Governance Trust answers:

"Was the process properly governed?"

33. Temporal Trust

Temporal Trust evaluates whether evidence remains valid over time.

Factors include:

evidence age;
replay age;
certificate expiry;
policy version;
regulation version;
freshness thresholds;
scheduled revalidation.

Temporal Trust answers:

"Is this trust assessment still current?"

34. Trust Vector Representation

A Trust Vector shall be represented as a structured artifact.

Illustrative example:

trust_vector:
  version: 1.0.0

  structural: 100
  semantic: 98
  computational: 100
  cryptographic: 100
  operational: 99
  replay: 100
  federation: 94
  ai: 97
  governance: 100
  temporal: 96

  overall: 98.4

The overall value is informative only.

The Trust Vector itself remains the authoritative representation.

35. Trust Vector Properties

Every Trust Vector shall satisfy the following properties:

deterministic;
reproducible;
explainable;
versioned;
lineage-aware;
replayable;
immutable after issuance;
independently computable.

Trust Vectors are never edited after creation.

Subsequent evaluations generate new Trust Vectors linked through the Trust Object lifecycle.

36. Trust Vector Versioning

Trust Vectors are versioned independently of TrustGate runtime versions.

Versioning captures changes to:

dimension definitions;
weighting methodology;
normalization methods;
aggregation strategies;
governance requirements.

Historical Trust Vectors shall always remain interpretable using the version under which they were produced.

37. Relationship to the Trust Object

The Trust Object is the canonical container for trust-related information.

The Trust Vector is a computed property of the Trust Object.

A Trust Object may therefore accumulate multiple Trust Vectors throughout its lifecycle as new evidence, replay events, federation exchanges, or governance decisions occur.

Each Trust Vector references:

evidence set;
validation results;
TG-VAL rules executed;
USO lineage;
producing MEIDs and CMIs;
policy version;
computation timestamp.

38. Transition to Trust Mathematics

The Canonical Trust Vector defines what is measured.

The next chapters introduces the mathematical framework governing:

evidence weighting;
confidence propagation;
uncertainty modelling;
corroboration;
contradiction handling;
decay functions;
aggregation algorithms.

Those mathematical models transform the Trust Object into the Canonical Trust Vector while preserving complete explainability and deterministic replay.

39. Trust Model Specification - Purpose

The Canonical Trust Model defines what trust is.

The Canonical Trust Vector defines what dimensions are measured.

This chapter defines how trust is computed.

The Trust Mathematics layer transforms evidence into deterministic, explainable, reproducible trust assessments while preserving complete computational lineage.

Every TrustGate engine implementing trust calculations shall conform to this specification.

40. Canonical Trust Computation Pipeline

Every Trust Object progresses through the same computational pipeline.

Evidence
↓
Validation
↓
Normalization
↓
Dimension Evaluation
↓
Confidence Assessment
↓
Uncertainty Assessment
↓
Evidence Corroboration
↓
Contradiction Analysis
↓
Dimension Scoring
↓
Trust Vector
↓
Trust Decision
↓
Trust Attestation

Every intermediate result is replayable.

41. Trust Object Identifier (TOID)

Every Trust Object shall possess a globally unique identifier.

Format:

TOID-ZYZ-000001

The Trust Object represents the complete lifecycle of trust associated with a computational artifact.

A Trust Object may accumulate:

multiple evidence sets;
multiple replay events;
multiple Trust Vectors;
multiple attestations;
multiple federation exchanges;
multiple governance decisions.

Unlike a Trust Vector, a Trust Object evolves throughout its lifetime.

41.1. TOID Characteristics

A TOID shall be:

globally unique;
immutable;
persistent;
replayable;
federatable;
lineage-aware.

TOIDs shall never be reused.

42. Trust Vector Identifier (TVID)

Every computed Trust Vector receives its own immutable identifier.

Format:

TVID-ZYZ-000001

A Trust Vector represents one specific trust evaluation.

Whenever trust changes, a new Trust Vector is generated.

The previous Trust Vectors remain immutable.

42.1. Relationship Between TOID and TVID

TOID-ZYZ-000021
│
├── TVID-ZYZ-000301
├── TVID-ZYZ-000547
├── TVID-ZYZ-000882
└── TVID-ZYZ-001012

One Trust Object therefore owns many historical Trust Vectors.

43. Canonical Trust Equation

Trust is computed from multiple dimensions.

Conceptually,

Trust Vector
=
f(
Evidence,
Validation,
Lineage,
Governance,
Replay,
Cryptography,
AI,
Federation,
Time
)

The exact implementation may evolve between Trust Model versions while preserving deterministic replay.

44. Confidence Model

Confidence represents how strongly the available evidence supports a trust conclusion.

Confidence is influenced by:

evidence quality;
evidence quantity;
independence;
corroboration;
validator quality;
verifier reputation;
replay success;
governance quality.

Confidence is never binary.

45. Uncertainty Model

Every trust assessment shall include explicit uncertainty.

Sources include:

incomplete evidence;
conflicting observations;
estimated values;
probabilistic calculations;
AI-derived conclusions;
external federation inputs.

Unknown information shall never be represented as certainty.

46. Evidence Weighting

Different evidence contributes differently to trust.

Illustrative hierarchy:

Evidence	Relative Weight
Digital signature	Very High
Replay verification	Very High
Accredited verifier	Very High
Independent corroboration	High
ERP transaction	High
Sensor telemetry	High
Manual submission	Medium
AI inference	Medium
User declaration	Medium
Estimated value	Low

Implementations may extend weighting rules through governance policies.

47. Corroboration

Independent evidence increases confidence.

Examples:

invoice matches ERP;
ERP matches ledger;
ledger matches emissions report;
emissions report matches verifier.

Corroboration strengthens trust without requiring identical sources.

48. Contradiction Handling

Contradictory evidence does not invalidate trust automatically.

Instead it generates contradiction events.

Examples:

inconsistent totals;
duplicate invoices;
conflicting measurements;
differing verifier opinions.

Contradictions become part of the Trust Object.

49. Trust Decay

Trust may decrease over time.

Typical causes include:

ageing evidence;
expired certificates;
outdated policies;
obsolete regulations;
replay becoming unavailable.

Trust decay shall always be explainable.

Automatic decay functions may be configured through governance policy.

50. Trust Recovery

Trust may increase when new evidence becomes available.

Examples:

replay succeeds;
verifier approval;
additional documentation;
federation confirmation;
updated measurements.

Trust recovery never modifies historical Trust Vectors.

Instead, a new Trust Vector is created.

51. Deterministic Trust

Two identical Trust Objects shall always produce identical Trust Vectors.

This invariant enables:

replay;
federation;
DAL verification;
AI explainability.

Any deviation constitutes a replay failure.

52. Trust Object Evolution

A Trust Object evolves through successive evaluations.

TOID-ZYZ-000021
│
├── Initial Evidence
│
├── TVID-001
│
├── Replay
│
├── TVID-002
│
├── Additional Evidence
│
├── TVID-003
│
├── Federation Verification
│
├── TVID-004
│
└── DAL Anchor

The Trust Object therefore represents the complete history of trust.

53. Canonical Relationships

The Trust Mathematics layer connects every canonical runtime artifact.

CMID
↓
CSI
↓
USO Instance
↓
MEID
↓
CMI
↓
TG-VAL
↓
TOID
↓
TVID
↓
Trust Decision
↓
Trust Attestation
↓
DAL
↓
Federation
↓
DSAIL

This chain guarantees end-to-end computational traceability.

54. Trust Model Invariants

The following invariants shall always hold.

TM-001

Every Trust Object shall possess exactly one TOID.

TM-002

Every Trust Vector shall possess exactly one TVID.

TM-003

Every Trust Vector shall reference exactly one Trust Object.

TM-004

Trust Vectors shall be immutable.

TM-005

Trust Objects shall preserve complete historical lineage.

TM-006

Trust computations shall be deterministic.

TM-007

Historical Trust Vectors shall remain reproducible indefinitely.

TM-008

Trust computations shall remain explainable.

TM-009

Every Trust Decision shall reference the originating TVID.

TM-010

Every Trust Attestation shall reference both the TOID and the TVID.

55. Transition to Governance & Operational Trust

The previous chapters have defined:

the ontology of trust;
the Trust Object;
the Canonical Trust Vector;
the mathematical model for trust computation.

The next chapters introduces the governance framework that controls how Trust Objects and Trust Vectors are created, versioned, approved, replayed, federated, and audited across the ZAYAZ ecosystem.

56 Governance & Operational Trust

These chapters defines how Trust Objects, Trust Vectors, Trust Decisions, and Trust Attestations are governed operationally across the ZAYAZ platform.

The previous chapters define:

what trust is;
how Trust Objects are structured;
how Trust Vectors represent trust;
how trust is computed.

This chapter defines how those artifacts are controlled, approved, versioned, replayed, federated, audited, revoked, and operationalized.

57. Governance Principle

Trust is not only computational.

Trust is also governed.

A technically valid trust result may still be unacceptable if it violates policy, lacks approval, fails segregation of duties, uses an expired verifier profile, or was produced under an unauthorized runtime configuration.

Therefore, every Trust Object and Trust Vector shall be evaluated under both:

Computational Validity
        +
Governance Validity
        =
Operational Trust

58. Governed Trust Artifacts

The Trust Model governs the following trust artifacts.

Artifact	Identifier	Governance Requirement
Trust Object	`TOID`	Lifecycle governance
Trust Vector	`TVID`	Immutable evaluation governance
Trust Decision	`decision_id`	Decision governance
Trust Attestation	`attestation_id`	Issuance governance
Replay Result	`replay_execution_id`	Replay governance
DAL Anchor	`dal_anchor`	Ledger governance
Federation Event	`federation_event_id`	Cross-ECO governance
AI Analysis	`ai_analysis_id`	AI governance

59. Trust Object Governance

A Trust Object is long-lived.

It accumulates evidence, Trust Vectors, replay results, attestations, federation events, and governance decisions over time.

Each Trust Object shall have:

one TOID;
one lifecycle state;
one current Trust Vector reference;
one governing policy context;
one owner or accountable organization;
complete lineage to CMID, CSI, USO, MEID, and CMI;
full audit history.

A Trust Object may evolve, but its TOID shall never change.

60. Trust Vector Governance

A Trust Vector is immutable.

Each Trust Vector shall have:

one TVID;
one parent TOID;
one trust model version;
one computation timestamp;
one producing MEID;
one producing CMI;
one policy context;
one replay hash or reproducibility reference where required.

Trust Vectors shall never be edited after creation.

If trust changes, a new Trust Vector is created.

61. Lifecycle States

Trust Objects may move through the following lifecycle.

CREATED
    ↓
ACTIVE
    ↓
UNDER_REVIEW
    ↓
ATTESTED
    ↓
FEDERATED
    ↓
ARCHIVED

Exceptional states include:

SUSPENDED REVOKED SUPERSEDED RETIRED

A revoked Trust Object remains historically available but may no longer be used as current trust evidence.

62. Trust Status Model

Trust status describes the current assurance state.

Status	Meaning
`PENDING`	Trust evaluation has not completed.
`VALIDATED`	Required validation checks passed.
`UNDER_REVIEW`	Human or assurance review is required.
`TRUSTED`	Trust threshold has been satisfied.
`ATTESTED`	A signed attestation has been issued.
`FEDERATED`	Trust artifact has been shared across ECO boundaries.
`REPLAYED`	Replay has verified the trust result.
`ARCHIVED`	Trust Object is retained for historical evidence.
`REVOKED`	Trust has been withdrawn.

63. Governance Decision Types

Governance may produce the following outcomes.

Decision	Meaning
`approve`	Trust artifact may proceed.
`reject`	Trust artifact is not accepted.
`review_required`	Human review is required.
`quarantine`	Artifact is isolated pending evidence.
`replay_required`	Replay must be performed before acceptance.
`attestation_required`	Formal attestation is required.
`dal_anchor_required`	Artifact must be anchored.
`federation_blocked`	Artifact may not be exchanged externally.
`revocation_required`	Existing trust must be revoked.

64. Approval Requirements

Approval is required when trust artifacts affect:

external disclosure;
federation exchange;
regulator-facing evidence;
DAL anchoring;
attestation issuance;
material ESG metrics;
assurance conclusions;
AI-assisted trust outcomes;
override decisions;
revocation events.

Approval shall preserve:

approver identity;
approval timestamp;
policy version;
reason;
affected TOID;
affected TVID;
audit event reference.

65. Segregation of Duties

Trust governance shall support segregation of duties.

The same actor should not be able to:

submit evidence;
validate evidence;
approve the Trust Vector;
issue attestation;
revoke review findings;
approve federation export.

Exceptions require explicit governance policy and audit logging.

66. Override Governance

Overrides may be allowed only through governed workflows.

Override records shall include:

override ID;
affected TOID;
affected TVID;
actor;
reason;
policy basis;
expiry;
scope;
risk classification;
approval status.

Overrides shall never delete original trust outcomes.

They create a new governed state.

67. Revocation Governance

Trust may be revoked when:

evidence is found invalid;
source identity is compromised;
signature verification fails;
replay fails;
verifier status changes;
federation partner is suspended;
material contradiction is confirmed;
policy violation is discovered.

Revocation shall create:

revocation event;
new Trust Vector;
audit event;
optional DAL anchor;
federation notification where applicable.

68. Replay Governance

Replay is a governance control.

Replay may be required when:

trust score exceeds materiality threshold;
attestation is issued;
federation export is requested;
contradiction is detected;
AI contributed materially;
policy version changes;
regulator or auditor requests evidence.

Replay shall reference the original TOID, original TVID, replay execution ID, replay profile, and replay result.

69. DAL Governance

DAL anchoring is required when trust evidence must be immutable and independently verifiable.

Typical DAL-required cases include:

signed trust attestations;
critical Trust Vectors;
replay proofs;
revocation events;
federation exchange proofs;
material approval decisions;
critical validation failures.

The DAL anchor shall reference both TOID and TVID where the anchor relates to a trust evaluation.

70. Federation Governance

Federated trust requires additional controls.

Before export, TrustGate shall verify:

federation profile;
receiving ECO permissions;
redaction profile;
trust status;
replay requirements;
DAL reference requirements;
signature requirements;
revocation status;
data minimization rules.

Federation shall never export internal-only trust diagnostics unless explicitly permitted by profile.

71. AI Governance

AI may support trust evaluation but shall not independently create authoritative trust.

AI contributions must be classified as:

AI Role Governance Treatment Advisory May support reviewer interpretation. Evidence extraction Requires validation. Pattern detection Requires corroboration. Risk flagging May trigger review. Score influence Requires policy-controlled weighting. Final decision Not permitted without human/governance approval.

AI-generated influence shall be traceable to ai_analysis_id, model CMI, prompt context, confidence, and validation status.

72. Operational Trust Controls

Operational Trust is affected by runtime quality.

The following runtime controls contribute to governance trust:

engine health;
configuration validity;
scheduler reliability;
retry behavior;
observability completeness;
audit logging availability;
queue health;
incident state;
safe-mode activation;
runtime policy drift.

A Trust Vector produced during degraded runtime may receive reduced Operational Trust or require review.

73. Trust Governance Events

Governance actions shall emit events.

Examples:

trust.object.created
trust.vector.created
trust.decision.approved
trust.decision.rejected
trust.override.applied
trust.replay.required
trust.attestation.issued
trust.dal.anchor.required
trust.federation.approved
trust.revoked

Each event shall preserve TOID, affected TVID, actor, timestamp, policy context, and audit reference.

74. Audit Requirements

Audit logging is mandatory for:

Trust Object creation;
Trust Vector creation;
decision approval;
manual override;
trust revocation;
attestation issuance;
federation export;
DAL anchoring;
replay failure;
critical policy exception;
AI-supported material decision.

Audit records shall be replayable and immutable according to TrustGate audit policy.

75. Governance Invariants

The following invariants shall always hold.

TGOV-001

Every Trust Object shall have exactly one current lifecycle state.

TGOV-002

Every Trust Vector shall be immutable after creation.

TGOV-003

Every Trust Decision shall reference a TVID.

TGOV-004

Every Trust Attestation shall reference both TOID and TVID.

TGOV-005

Every material governance action shall create an audit event.

TGOV-006

Overrides shall never delete original trust evidence.

TGOV-007

Revocation shall preserve historical Trust Vectors.

TGOV-008

Federation export shall require federation profile validation.

TGOV-009

AI shall not independently issue authoritative trust decisions.

TGOV-010

Replay-critical Trust Vectors shall remain reproducible.

76. Transition to Trust State Machine - The Canonical Trust State Machine

The next chapters defines the canonical Trust State Machine, including state transitions, allowed transitions, invalid transitions, and how Trust Objects evolve from observation to validation, trust, attestation, anchoring, federation, archive, and revocation.

The Canonical Trust State Machine defines the lifecycle of every Trust Object (TOID) throughout the ZAYAZ platform.

A Trust Object is never static.

It evolves as new evidence is collected, validations are executed, governance decisions are made, replay operations are performed, and attestations are issued.

This specification establishes the only valid lifecycle for Trust Objects and defines the permissible transitions between trust states.

Every TrustGate implementation shall conform to this state model.

77. Trust State Machine Overview

Every Trust Object follows a governed lifecycle.

                 ┌──────────────┐
                 │   CREATED    │
                 └──────┬───────┘
                        │
                        ▼
                 ┌──────────────┐
                 │ VALIDATING   │
                 └──────┬───────┘
                        │
          ┌─────────────┴─────────────┐
          ▼                           ▼
   VALIDATED                  VALIDATION_FAILED
          │                           │
          ▼                           │
 UNDER_REVIEW ◄───────────────────────┘
          │
          ▼
     TRUSTED
          │
          ▼
     ATTESTED
          │
          ▼
   DAL_ANCHORED
          │
          ▼
    FEDERATED
          │
          ▼
    ARCHIVED

Exceptional transitions:

ACTIVE LIFECYCLE

CREATED
↓

VALIDATING
↓

VALIDATED
↓

UNDER_REVIEW
↓

TRUSTED
↓

ATTESTED
↓

DAL_ANCHORED
↓

FEDERATED
↓

ARCHIVED


EXCEPTION STATES

VALIDATION_FAILED
REVOKED
SUPERSEDED
SUSPENDED
RETIRED

78. Canonical Trust States

A Trust Object always occupies exactly one primary lifecycle state.

Lifecycle states represent the long-term evolution of trust and shall not be used to represent temporary operational activities.

Operational activities are represented by Operational Trust Flags (Section 79).

CREATED

The Trust Object has been created.

Characteristics:

TOID assigned
no Trust Vector yet
awaiting validation
lineage established

VALIDATING

TrustGate is executing validation rules.

Typical activities:

TG-VAL execution
schema validation
identity verification
structural verification
semantic verification

VALIDATED

Required validations completed successfully.

Characteristics:

validation passed
Trust Vector may be generated
governance evaluation may begin

VALIDATION_FAILED

One or more mandatory validation rules failed.

Characteristics:

trust not established
failure preserved
replay permitted
remediation possible

UNDER_REVIEW

Manual or governed review is required.

Typical reasons:

conflicting evidence
AI uncertainty
policy requirement
federation requirement
regulator request

TRUSTED

TrustGate has determined that trust requirements have been satisfied.

Characteristics:

Trust Vector issued
trust decision available
eligible for attestation

ATTESTED

A governed Trust Attestation has been issued.

Characteristics:

signed
immutable
auditable

DAL_ANCHORED

Trust artifacts have been anchored to the Digital Assurance Ledger.

Characteristics:

immutable anchor
cryptographic verification
long-term preservation

FEDERATED

Trust has been exchanged with another ECO.

Characteristics:

federation profile applied
federation event recorded
interoperability established

ARCHIVED

Trust Object has completed its operational lifecycle.

Historical replay remains possible.

REVOKED

Trust has been withdrawn.

Reasons include:

invalid evidence
revoked verifier
replay failure
compromised signature
governance decision

Revocation never deletes historical Trust Vectors.

SUPERSEDED

A newer Trust Object replaces the current one.

Example:

Corrected emissions inventory replacing a previous submission.

Historical lineage is preserved.

79 Operational Trust Flags (OTF)

Operational Trust Flags represent temporary conditions, pending actions, or runtime requirements associated with a Trust Object.

Unlike lifecycle states, multiple Operational Trust Flags may be active simultaneously.

Operational Trust Flags shall never replace the primary lifecycle state.

For example:

Lifecycle State

TRUSTED

Operational Trust Flags

✓ replay_required

✓ dal_anchor_required

✓ federation_validation_required

79.1. Characteristics

Operational Trust Flags are:

temporary;
replayable;
independently auditable;
policy-driven;
additive;
removable once resolved.

They do not change the Trust Object lifecycle.

79.2. Canonical Flags

Flag	Meaning
replay_required	Replay must be executed before progression.
manual_review_required	Human review is required.
quarantine_required	Trust Object is temporarily isolated.
attestation_required	Trust Attestation must be issued.
dal_anchor_required	DAL anchoring is pending.
federation_validation_required	Federation policy validation is required.
ai_review_required	AI findings require confirmation.
contradiction_detected	Contradictory evidence exists.
evidence_missing	Required evidence is incomplete.
governance_exception	Governance policy exception exists.
revocation_review_required	Trust is under revocation assessment.

Additional flags may be introduced through governance policies without changing the canonical Trust State Machine.

79.3. Operational Trust Flag Lifecycle

Operational Trust Flags follow a simple lifecycle.

Created
↓
Active
↓
Resolved
↓
Archived

Historical Operational Trust Flags shall remain available for replay and audit.

79.4. Multiple Concurrent Flags

A Trust Object may possess multiple active flags simultaneously.

Example:

TOID-ZYZ-000431

Lifecycle State

TRUSTED

Operational Trust Flags

✓ replay_required

✓ dal_anchor_required

✓ ai_review_required

The existence of one flag shall not prevent additional flags from being applied.

80. Canonical State Transitions

Current State	Allowed Next States
CREATED	VALIDATING
VALIDATING	VALIDATED, VALIDATION_FAILED
VALIDATION_FAILED	VALIDATING, UNDER_REVIEW
VALIDATED	UNDER_REVIEW, TRUSTED
UNDER_REVIEW	TRUSTED, REVOKED
TRUSTED	ATTESTED, REVOKED
ATTESTED	DAL_ANCHORED
DAL_ANCHORED	FEDERATED, ARCHIVED
FEDERATED	ARCHIVED
REVOKED	ARCHIVED
SUPERSEDED	ARCHIVED
SUSPENDED	ACTIVE, RETIRED
RETIRED	(terminal)
ARCHIVED	(terminal)

All operational workflows such as replay, DAL anchoring requests, federation validation, or manual review shall be represented by Operational Trust Flags rather than lifecycle transitions.

81. Transition Events

Each state transition shall emit a canonical TrustGate event.

Examples include:

trust.created
trust.validation.started
trust.validation.completed
trust.validation.failed
trust.review.started
trust.review.completed
trust.trusted
trust.attested
trust.dal.anchored
trust.federated
trust.archived
trust.revoked
trust.superseded
trust.replay.required
trust.replay.completed

Every event shall reference:

TOID
current TVID (if applicable)
triggering actor or engine
timestamp
MEID
CMI
policy version
audit event identifier

82. Trust Vector Evolution

A Trust Object may accumulate multiple Trust Vectors throughout its lifecycle.

TOID-ZYZ-000014
│
├── TVID-001
│     Initial Validation
│
├── TVID-002
│     Replay Verification
│
├── TVID-003
│     Additional Evidence
│
├── TVID-004
│     Human Review
│
└── TVID-005
      Federation Approval

Each Trust Vector captures the trust state at a specific evaluation point.

Historical Trust Vectors remain immutable.

83. Replay Operations

Replay is an operational activity rather than a lifecycle state.

When replay is required, the Trust Object remains in its current lifecycle state while the replay_required Operational Trust Flag is set.

Example:

Lifecycle State

TRUSTED

Operational Trust Flags

✓ replay_required

Following successful replay:

the Operational Trust Flag is removed;
a new Trust Vector (TVID) is generated;
the existing Trust Object (TOID) remains unchanged;
complete replay lineage is preserved.

Replay therefore represents a new trust evaluation rather than a lifecycle transition.

84. Revocation Flow

Trust revocation shall follow a controlled lifecycle.

TRUSTED
↓
UNDER_REVIEW
↓
REVOKED
↓
ARCHIVED

Revocation shall preserve:

original Trust Object;
all historical Trust Vectors;
revocation reason;
governing policy;
audit trail;
DAL references where applicable.

85. Federation Operations

Federation is governed through both lifecycle progression and Operational Trust Flags.

Prior to federation, a Trust Object may receive the federation_validation_required Operational Trust Flag.

Once federation validation has completed successfully:

the Operational Trust Flag is removed;
federation governance is recorded;
the Trust Object progresses to the FEDERATED lifecycle state;
a new Trust Vector may be generated if federation materially affects the trust assessment.

86. State Invariants

The following invariants shall always hold.

TSM-001

A Trust Object shall occupy exactly one primary lifecycle state at any point in time.

TSM-002

A Trust Object may possess zero, one, or multiple active Operational Trust Flags.

TSM-003

Every state transition shall create an immutable audit event.

TSM-004

Every Trust Vector shall be associated with exactly one Trust Object.

TSM-005

State transitions shall never modify historical Trust Vectors.

TSM-006

Revocation shall preserve complete lineage.

TSM-007

Replay shall generate a new Trust Vector rather than modifying an existing one.

TSM-008

Attestation shall only occur from the TRUSTED state.

TSM-009

DAL anchoring shall only occur for attested or otherwise governance-approved Trust Objects.

TSM-010

Every archived Trust Object shall remain fully replayable unless retention policies explicitly permit cryptographic retirement.

87. Relationship to the TrustGate Architecture

The Canonical Trust State Machine integrates with all major TrustGate components.

Component	Relationship
Trust Object Registry (`zar.trust_object_registry`)	Stores the current lifecycle state for each `TOID`.
Trust Vector Registry (`zar.trust_vector_registry`)	Stores immutable `TVID` snapshots created at state transitions.
Validation Rule Registry	Governs transitions into `VALIDATED` or `VALIDATION_FAILED`.
Replay Engine	Executes and governs replay-related transitions.
Scheduler / Orchestrator	Coordinates asynchronous transition workflows.
Runtime Configuration Engine	Applies policy-driven transition rules and thresholds.
Observability Engine	Emits telemetry and metrics for all state changes.
DAL	Anchors trust artifacts at governed lifecycle stages.
DSAIL	Learns from state transitions and trust evolution.
EGFS	Extends the state machine across federated ecosystems.

88. Transition to Operational Trust Intelligence

The Canonical Trust State Machine defines how trust evolves.

The next chapters introduces Operational Trust Intelligence, where Trust Objects, Trust Vectors, runtime telemetry, replay history, AI analysis, and federation outcomes are synthesized into higher-level trust analytics, predictive insights, anomaly detection, and continuous assurance across the ZAYAZ ecosystem.

89. Operational Trust Intelligence

Operational Trust Intelligence (OTI) transforms individual trust evaluations into platform-wide intelligence.

Where previous chapters define the evaluation of individual Trust Objects (TOID) and Trust Vectors (TVID), Operational Trust Intelligence analyses trust collectively across organizations, engines, datasets, execution histories, federation boundaries, and time.

Its purpose is to answer questions such as:

Where is trust increasing?
Where is trust degrading?
Which engines consistently produce the highest-quality trust?
Which suppliers repeatedly generate poor trust?
Which validation rules generate the most failures?
Which datasets require replay?
Which organizations require additional assurance?

Operational Trust Intelligence enables TrustGate to evolve from an assurance engine into a continuously learning trust ecosystem.

90. Principles

Operational Trust Intelligence shall be:

evidence-driven;
computationally reproducible;
policy-governed;
explainable;
privacy-preserving;
federation-aware;
AI-assisted but not AI-dependent.

No intelligence output shall alter historical trust evidence.

Intelligence augments trust—it does not replace it.

91. Intelligence Layers

Operational Trust Intelligence operates across multiple analytical layers.

Layer	Scope	Primary Question
Object Intelligence	Single TOID	Is this object trustworthy?
Execution Intelligence	Runtime execution	Was this execution trustworthy?
Engine Intelligence	MEID / CMI	Which engines produce the highest-quality trust?
Domain Intelligence	ESG domain	Which sustainability domains exhibit systemic trust issues?
Organization Intelligence	Company / Tenant	How trustworthy is this organization over time?
Federation Intelligence	ECO ecosystem	How does trust propagate across federations?
Platform Intelligence	Entire ZAYAZ platform	What are the global trust trends?

92. Canonical Trust Analytics

TrustGate shall support standardized analytical views.

Examples include:

Trust Score Distribution
Trust Trend Analysis
Validation Failure Analysis
Replay Frequency Analysis
Engine Reliability Analysis
Federation Success Analysis
Assurance Coverage
Policy Compliance
Data Quality Trends
Runtime Stability
Trust Decay
Trust Volatility
Confidence Distribution
Governance Effectiveness
Attestation Success Rate

These analytical products are derived from canonical trust artifacts and remain fully reproducible.

93. Trust Intelligence Dimensions

Operational Trust Intelligence analyses multiple dimensions simultaneously.

Structural Trust
↓
Semantic Trust
↓
Computational Trust
↓
Operational Trust
↓
Governance Trust
↓
Replay Trust
↓
Federation Trust
↓
AI Trust
↓
Temporal Trust
↓
Composite Platform Trust

Each dimension contributes to a broader understanding of trust across the ecosystem.

94. Trust Trend Analysis

Trust is temporal.

Operational Trust Intelligence continuously evaluates changes in trust over time.

Examples include:

improving trust;
declining trust;
unstable trust;
cyclic trust patterns;
seasonal trust variations;
policy-driven trust changes.

Trend analysis shall operate at all intelligence layers.

95. Trust Drift Detection

Trust characteristics may evolve due to changes in:

runtime configuration;
validation rules;
engine versions;
governance policies;
data quality;
federation relationships;
external regulatory requirements.

Operational Trust Intelligence shall detect statistically significant trust drift and generate alerts when configured thresholds are exceeded.

96. Trust Hotspot Detection

TrustGate shall identify concentrations of elevated operational risk.

Examples include:

repeated validation failures;
frequent replay requirements;
low-confidence AI outcomes;
excessive governance overrides;
federation synchronization failures;
recurring DAL anchoring failures.

Hotspots support proactive assurance planning.

97. Trust Benchmarking

Trust may be benchmarked across multiple dimensions.

Examples:

engine vs engine;
organization vs organization;
supplier vs supplier;
facility vs facility;
country vs country;
reporting period vs reporting period;
ESG framework vs ESG framework.

Benchmarking shall respect tenant isolation and federation governance.

98. Trust Intelligence Graph

Operational Trust Intelligence maintains a dynamic graph of trust relationships.

Nodes may include:

TOID;
TVID;
CMID;
CSI;
USO Instance;
MEID;
CMI;
Validation Rule;
Organization;
Facility;
Supplier;
Product;
Report;
Policy.

Edges represent:

produced by;
validated by;
replayed by;
attested by;
derived from;
federated to;
supersedes;
contradicts;
supports;
references.

The Trust Intelligence Graph enables lineage-aware analytics and graph-based reasoning.

99. Trust Risk Indicators

Operational Trust Intelligence derives Key Trust Indicators (KTIs).

Examples include:

Indicator	Description
Replay Frequency	Percentage of Trust Objects requiring replay.
Validation Failure Rate	Failed validations per reporting period.
Average Trust Score	Mean overall Trust Vector score.
Trust Volatility	Degree of trust fluctuation over time.
Governance Override Rate	Frequency of manual overrides.
Federation Success Rate	Successful federated exchanges.
DAL Coverage	Percentage of Trust Objects anchored in DAL.
Attestation Coverage	Percentage of trusted objects formally attested.

KTIs provide operational insight into the health of the assurance ecosystem.

100. Predictive Trust Intelligence

Historical trust patterns may be used to estimate future trust conditions.

Examples include:

predicted replay requirements;
anticipated validation failures;
expected trust degradation;
supplier risk forecasting;
audit workload estimation;
federation readiness prediction.

Predictive outputs shall always be distinguished from observed trust evidence.

101. AI-Assisted Trust Intelligence

Artificial Intelligence may enhance Operational Trust Intelligence by supporting:

anomaly detection;
clustering;
trend identification;
semantic similarity analysis;
recommendation generation;
confidence estimation;
root cause analysis;
summarization of trust evidence.

AI-generated intelligence shall remain explainable and fully traceable to source artifacts.

102. Trust Intelligence Outputs

Operational Trust Intelligence produces reusable intelligence artifacts, including:

Trust Dashboards;
Trust Heatmaps;
Trust Trend Reports;
Trust Forecasts;
Replay Recommendations;
Assurance Prioritization Lists;
Validation Improvement Suggestions;
Federation Readiness Assessments;
Governance Exception Reports;
AI Insight Reports.

Each output shall reference the underlying Trust Objects and Trust Vectors from which it was derived.

103. Operational Trust Intelligence Invariants

The following invariants shall always hold.

OTI-001

Operational Trust Intelligence shall never modify historical Trust Objects.

OTI-002

Operational Trust Intelligence shall never modify historical Trust Vectors.

OTI-003

All intelligence outputs shall be reproducible from canonical trust artifacts.

OTI-004

AI-generated insights shall be clearly distinguishable from authoritative trust evidence.

OTI-005

Operational Trust Intelligence shall preserve tenant isolation unless federation policies explicitly permit aggregation.

OTI-006

Predictive intelligence shall never overwrite observed trust.

OTI-007

Every intelligence artifact shall preserve complete lineage to the Trust Objects and Trust Vectors from which it was derived.

104. Relationship to the ZAYAZ Architecture

Operational Trust Intelligence serves as the analytical layer above TrustGate’s operational assurance services.

It integrates with:

Component	Relationship
Trust Object Registry	Primary source of governed trust entities (`TOID`).
Trust Vector Registry	Historical trust evaluations (`TVID`).
Validation Rule Registry	Source of validation outcomes and compliance metrics.
Replay Engine	Replay history, reproducibility, and replay quality indicators.
DAL	Immutable anchoring status and cryptographic evidence.
DSAIL	Machine learning, predictive analytics, and adaptive trust models.
Scheduler / Orchestrator	Execution telemetry and workflow analytics.
Runtime Observability Engine	Runtime health, performance, and operational signals.
Federation Profiles	Cross-ECO trust intelligence and interoperability.

105. Transition to the Trust Intelligence Ecosystem

This chapter defined how TrustGate transforms operational assurance data into actionable intelligence.

The next chapter extends this capability by introducing the Trust Intelligence Ecosystem, where Operational Trust Intelligence, DSAIL, DAL, EGFS, and federation-aware analytics operate together to create a continuously improving, globally interoperable trust platform.

106. Trust Intelligence Objects (TG-INTEL)

Operational Trust Intelligence (Part 7) defines how TrustGate analyses trust across the platform.

This chapter defines how those analytical outputs are represented as canonical TrustGate artifacts.

Rather than treating analytics as transient dashboards or reports, TrustGate materializes significant analytical outcomes as Trust Intelligence Objects (TG-INTEL).

A Trust Intelligence Object is an immutable, versioned, replayable intelligence artifact derived from one or more canonical trust artifacts.

TG-INTEL artifacts are derived analytical artifacts and are not themselves primary trust evidence.

TG-INTEL transforms analytical insight into a governed platform asset.

107. Definition

A Trust Intelligence Object (TG-INTEL) is a governed artifact representing derived knowledge produced by TrustGate analytics.

Unlike a Trust Object (TOID), which represents a governed assurance entity, or a Trust Vector (TVID), which captures a point-in-time trust evaluation, a TG-INTEL artifact represents higher-order analytical conclusions.

Examples include:

trust trends;
anomaly detections;
replay recommendations;
benchmark analyses;
federation readiness assessments;
validation optimization suggestions;
governance risk analyses;
AI-assisted trust insights.

TG-INTEL artifacts are derived—not primary evidence.

108. Position within the Trust Model

The canonical trust hierarchy is:

Business Evidence
        │
        ▼
Trust Object (TOID)
        │
        ▼
Trust Vector (TVID)
        │
        ▼
Operational Trust Intelligence
        │
        ▼
Trust Intelligence Object (TG-INTEL)
        │
        ▼
Governance Decision
        │
        ▼
Replay / Federation / AI Learning

Each layer builds upon the previous while preserving complete traceability.

109. Trust Intelligence Object Types

TrustGate defines the following canonical intelligence categories.

Category	Purpose
Trend Analysis	Longitudinal trust evolution
Anomaly Detection	Identification of abnormal trust behavior
Benchmark	Comparative trust analysis
Forecast	Predictive trust estimation
Recommendation	Suggested operational or governance actions
Risk Assessment	Derived operational trust risks
Replay Intelligence	Replay prioritization and optimization
Validation Intelligence	Validation rule effectiveness
Federation Intelligence	Cross-ECO trust analytics
Governance Intelligence	Governance quality and policy effectiveness
AI Insight	Explainable AI-derived analytical observations

Additional categories may be introduced through future TrustGate specifications.

110. Canonical Identifier

Every Trust Intelligence Object shall receive a canonical identifier.

Format:

TG-INTEL-000001

Examples:

TG-INTEL-000014
Supplier Trust Trend Analysis

TG-INTEL-000122
Scope 3 Replay Recommendation

TG-INTEL-001532
Federation Readiness Assessment

TG-INTEL-004112
Validation Rule Optimization

Identifiers are immutable and globally unique.

111. Canonical Structure

Each Trust Intelligence Object shall contain:

TG-INTEL identifier;
intelligence category;
title;
description;
producing MEID;
producing CMI;
intelligence model version;
creation timestamp;
source Trust Objects (TOID);
source Trust Vectors (TVID);
source Validation Rules;
source Replay Events;
confidence score;
explainability summary;
lineage metadata;
governance status;
lifecycle state.

This ensures every intelligence artifact is reproducible and fully traceable.

112. Intelligence Lineage

A Trust Intelligence Object shall preserve lineage to all contributing artifacts.

TOID-001
     │
TVID-002
     │
Replay Event
     │
Validation Results
     │
Scheduler Metrics
     │
Observability Signals
     │
DSAIL Analysis
     │
TG-INTEL-004211

The intelligence lineage forms part of the Universal Signal Ontology and shall be replayable.

113. Intelligence Lifecycle

Every TG-INTEL artifact progresses through a governed lifecycle.

GENERATED
      │
      ▼
VALIDATED
      │
      ▼
PUBLISHED
      │
      ▼
SUPERSEDED
      │
      ▼
ARCHIVED

Exceptional states include:

REJECTED
REVOKED
DEPRECATED

Historical intelligence artifacts remain available for audit and replay.

114. Explainability

Every Trust Intelligence Object shall include an explainability profile describing:

contributing trust dimensions;
source artifacts;
analytical methods;
statistical assumptions;
AI involvement;
confidence intervals;
uncertainty estimates;
governance considerations.

Explainability enables regulators, auditors, and users to understand how analytical conclusions were produced.

115. Relationship to DSAIL

TG-INTEL artifacts provide structured inputs to the Distributed Sustainability AI Learning (DSAIL) framework.

DSAIL may:

learn from historical intelligence artifacts;
improve analytical models;
detect emerging trust patterns;
recommend governance enhancements.

However, DSAIL shall not modify existing TG-INTEL artifacts. New analytical conclusions shall result in new TG-INTEL identifiers.

116. Relationship to the ZAYAZ Architecture

Trust Intelligence Objects integrate with multiple architectural components.

Component	Relationship
Trust Object Registry	Primary source of assurance entities.
Trust Vector Registry	Source of trust evaluations.
Validation Rule Registry	Source of validation evidence.
Replay Specification	Source of replay analytics.
Runtime Observability Engine	Source of operational telemetry.
Scheduler / Orchestrator	Source of execution intelligence.
DAL	Optional anchoring of published intelligence artifacts.
EGFS	Federation of selected intelligence products across trusted ecosystems.
DSAIL	Learning, prediction, and adaptive trust analytics.

117. Canonical Invariants

The following invariants shall always hold.

TGINT-001

Every Trust Intelligence Object shall have exactly one immutable TG-INTEL identifier.

TGINT-002

Every TG-INTEL artifact shall preserve complete lineage to its source Trust Objects and Trust Vectors.

TGINT-003

TG-INTEL artifacts shall be reproducible from canonical source artifacts.

TGINT-004

Published intelligence shall never modify historical trust evidence.

TGINT-005

AI-assisted intelligence shall be distinguishable from deterministic analytical results.

TGINT-006

Superseded TG-INTEL artifacts shall remain available for replay and audit.

118. Canonical Trust Architecture diagram

Business Evidence
        │
        ▼
Signal (CSI)
        │
        ▼
USO Instance
        │
        ▼
Validation
        │
        ▼
Trust Object (TOID)
        │
        ▼
Trust Vector (TVID)
        │
        ▼
Trust Intelligence (TG-INTEL)
        │
        ▼
Trust Attestation
        │
        ▼
DAL
        │
        ▼
Federation
        │
        ▼
DSAIL

GitHub Repo Request for Change (RFC)

1. Purpose​

2. Scope​

3. Vision​

4. Design Principles​

4.1. Evidence Before Trust​

4.3. Deterministic Reproducibility​

4.4. Complete Lineage​

4.5. Federation Neutrality​

4.6. Human Oversight​

5. Relationship to TrustGate​

6. Relationship to Other Specifications​

7. Canonical Trust Architecture​

8. Trust as a Vector​

9. Conformance​

10. References​

11. Introduction​

12. Trust as a Computational Object​

13. Canonical Definition of Trust​

14. Canonical Trust Components​

14.1. Evidence​

14.2. Provenance​

14.3. Lineage​

14.4. Integrity​

14.5. Authenticity​

14.6. Consistency​

14.7. Completeness​

14.8. Timeliness​

14.9. Governance​

15. Trust Ontology​

16. Trust Relationships​

17. Trust Lifecycle​

18. Trust Invariants​

TI-009​

TI-010​

19. Relationship to Canonical Artifacts​

20. Transition to the Trust Dimensions​

21. Purpose​

22. Trust Vector Architecture​

23. Canonical Trust Dimensions​

24. Structural Trust​

25. Semantic Trust

26. Computational Trust​

27. Cryptographic Trust​

28. Operational Trust​

29. Replay Trust​

30. Federation Trust​

31. AI Trust​

32. Governance Trust​

33. Temporal Trust​

34. Trust Vector Representation​

35. Trust Vector Properties​

36. Trust Vector Versioning​

37. Relationship to the Trust Object​

38. Transition to Trust Mathematics​

39. Trust Model Specification - Purpose​

40. Canonical Trust Computation Pipeline​

41. Trust Object Identifier (TOID)​

41.1. TOID Characteristics​

42. Trust Vector Identifier (TVID)​

42.1. Relationship Between TOID and TVID​

43. Canonical Trust Equation​

44. Confidence Model​

45. Uncertainty Model​

46. Evidence Weighting​

47. Corroboration​

48. Contradiction Handling​

49. Trust Decay​

50. Trust Recovery​

51. Deterministic Trust​

52. Trust Object Evolution​

53. Canonical Relationships​

54. Trust Model Invariants​

55. Transition to Governance & Operational Trust​

56 Governance & Operational Trust​

57. Governance Principle​

58. Governed Trust Artifacts​

59. Trust Object Governance​

60. Trust Vector Governance​

61. Lifecycle States​

62. Trust Status Model​

1. Purpose

2. Scope

3. Vision

4. Design Principles

4.1. Evidence Before Trust

4.3. Deterministic Reproducibility

4.4. Complete Lineage

4.5. Federation Neutrality

4.6. Human Oversight

5. Relationship to TrustGate

6. Relationship to Other Specifications

7. Canonical Trust Architecture

8. Trust as a Vector

9. Conformance

10. References

11. Introduction

12. Trust as a Computational Object

13. Canonical Definition of Trust

14. Canonical Trust Components

14.1. Evidence

14.2. Provenance

14.3. Lineage

14.4. Integrity

14.5. Authenticity

14.6. Consistency

14.7. Completeness

14.8. Timeliness

14.9. Governance

15. Trust Ontology

16. Trust Relationships

17. Trust Lifecycle

18. Trust Invariants

TI-009

TI-010

19. Relationship to Canonical Artifacts

20. Transition to the Trust Dimensions

21. Purpose

22. Trust Vector Architecture

23. Canonical Trust Dimensions

24. Structural Trust

26. Computational Trust

27. Cryptographic Trust

28. Operational Trust

29. Replay Trust

30. Federation Trust

31. AI Trust

32. Governance Trust

33. Temporal Trust

34. Trust Vector Representation

35. Trust Vector Properties

36. Trust Vector Versioning

37. Relationship to the Trust Object

38. Transition to Trust Mathematics

39. Trust Model Specification - Purpose

40. Canonical Trust Computation Pipeline

41. Trust Object Identifier (TOID)

41.1. TOID Characteristics

42. Trust Vector Identifier (TVID)

42.1. Relationship Between TOID and TVID

43. Canonical Trust Equation

44. Confidence Model

45. Uncertainty Model

46. Evidence Weighting

47. Corroboration

48. Contradiction Handling

49. Trust Decay

50. Trust Recovery

51. Deterministic Trust

52. Trust Object Evolution

53. Canonical Relationships

54. Trust Model Invariants

55. Transition to Governance & Operational Trust

56 Governance & Operational Trust

57. Governance Principle

58. Governed Trust Artifacts

59. Trust Object Governance

60. Trust Vector Governance

61. Lifecycle States

62. Trust Status Model

63. Governance Decision Types