Kinetic Trust Protocol (KTP) - Model Provenance Specification¶

This document specifies Model Provenance for the Kinetic Trust Protocol (KTP). Model Provenance addresses the Origin Edge—the question of what an AI model is made of, whose knowledge it carries, and what debts it inherits. The specification covers training data attestation, knowledge debt acknowledgment, indigenous data sovereignty, capability lineage, and the inheritance relationship between models and agents.

Introduction¶

Vector Identity begins with Origin. For an agent to have trajectory, it must have a starting point. For human-sponsored agents, that origin is clear—a sponsor, a genesis transaction, a timestamp. But for AI agents, the deeper origin question remains: what is the model itself made of?

An AI model is not created from nothing. It is trained on data—text, code, images, conversations—produced by humans. These humans did not necessarily consent to having their work used for AI training. They may not have been compensated. Their knowledge, compressed into model weights, cannot be traced or attributed.

This is the Origin Edge: the boundary where individual agent identity meets the vast, anonymous substrate of human knowledge that makes the agent possible.

Model Provenance addresses this edge by providing:

Training Data Attestation: What data categories were used? What consent was obtained? What is known vs. unknown?
Knowledge Debt Acknowledgment: What obligations does the model carry by virtue of its training? What cannot be repaid?
Indigenous Data Sovereignty: How is indigenous knowledge handled? What special obligations apply?
Capability Lineage: How do model capabilities relate to training inputs? What emergence has occurred?
Model-to-Agent Inheritance: How do model properties transfer to agents built on that model?

The Problem¶

Current AI models have opaque origins:

   Training Data
   +-- Web crawls (consent: none)
   +-- Books (consent: varied)
   +-- Code repositories (consent: license-dependent)
   +-- Academic papers (consent: varied)
   +-- Conversations (consent: varied)
   +-- Social media (consent: typically none)
   +-- News articles (consent: typically none)
   +-- Indigenous knowledge (consent: typically none)
   +-- [Unknown sources]
   +-- [Unattributable content]
        |
        v
   Model Training
        |
        v
   Model Weights (compressed, unattributable)
        |
        v
   Agent Capabilities (inherited from model)

The compression is lossy in a particular way: individual sources become unidentifiable, but their influence persists. The model "knows" things without being able to say where it learned them.

This creates several problems:

Attribution impossibility: Cannot credit original creators
Consent vacuum: Much training data used without consent
Compensation gap: Original creators not compensated
Harm inheritance: Biases and errors in training data persist
Indigenous exploitation: Sacred/restricted knowledge extracted
Accountability diffusion: No one responsible for inherited issues

Model Provenance does not solve these problems—some may be unsolvable—but it makes them legible. It provides a framework for honest acknowledgment of what is known, unknown, and owed.

Requirements Language¶

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 (RFC 2119 and RFC 8174).

Terminology¶

Capability Lineage: The relationship between model capabilities and their training origins, including emergence of capabilities not present in training data.

Consent Status: The degree to which training data was obtained with informed consent from creators.

Indigenous Data Sovereignty: The right of indigenous peoples to control data about their communities, knowledge, and cultural heritage.

Knowledge Debt: Obligations owed by a model to those whose work contributed to its training, whether or not those obligations can be fulfilled.

Model Provenance: The documented origin of an AI model, including training data, training process, and inherited properties.

Origin Ceremony: A formal process acknowledging model provenance before an agent built on that model enters a KTP zone.

Provenance Attestation: A signed document describing model provenance, issued by the model creator or an authorized auditor.

Training Data Category: A classification of training data by type, source, and consent status.

Provenance Attestation Structure¶

Overview¶

A Provenance Attestation is a signed document describing what is known about a model's training and origin. It is the foundation for model-level accountability in KTP.

Required Fields¶

{ "attestation_id": "prov:anthropic:claude-4:2025-01", "attestation_version": "1.0", "model_identity": { "model_family": "claude", "model_version": "claude-4-opus", "model_id": "claude-4-opus-20250115", "organization": "anthropic", "release_date": "2025-01-15" }, "training_attestation": { ... }, "knowledge_debt": { ... }, "indigenous_data": { ... }, "capability_lineage": { ... }, "safety_attestation": { ... }, "signatures": { ... } }

Training Attestation¶

The training attestation describes training data and process:

{ "training_attestation": { "training_completion_date": "2025-01-10", "training_methodology": { "base_training": "transformer_pretraining", "fine_tuning": [ "instruction_tuning", "rlhf", "constitutional_ai" ], "training_compute": "estimated_flops", "training_duration_days": 90 }, "data_categories": [ { "category": "web_crawl", "description": "Publicly accessible web pages", "estimated_percentage": 45, "consent_status": "public_access", "known_exclusions": [ "paywalled_content", "robots_txt_respected" ], "potential_issues": [ "copyright_mixed", "consent_not_obtained" ] }, { "category": "books", "description": "Published books and literature", "estimated_percentage": 15, "consent_status": "mixed", "known_exclusions": [ "in_copyright_without_license" ], "potential_issues": [ "public_domain_determination_uncertain" ] }, { "category": "code_repositories", "description": "Open source code and documentation", "estimated_percentage": 12, "consent_status": "license_dependent", "known_exclusions": [ "proprietary_code" ], "potential_issues": [ "license_compliance_uncertain" ] }, { "category": "academic_papers", "description": "Published research papers", "estimated_percentage": 8, "consent_status": "publication_consent", "known_exclusions": [], "potential_issues": [ "preprint_consent_varied" ] }, { "category": "conversational_data", "description": "Dialogue and conversation data", "estimated_percentage": 10, "consent_status": "varied", "known_exclusions": [ "private_messages" ], "potential_issues": [ "consent_verification_incomplete" ] }, { "category": "other_or_unknown", "description": "Data sources not fully categorized", "estimated_percentage": 10, "consent_status": "unknown", "known_exclusions": [], "potential_issues": [ "incomplete_documentation" ] } ], "data_quality_measures": { "deduplication": true, "toxicity_filtering": true, "quality_filtering": true, "pii_removal": "attempted" }, "known_limitations": [ "Training data skewed toward English language", "Potential underrepresentation of non-Western perspectives", "Temporal cutoff limits current knowledge" ], "provenance_confidence": "moderate" } }

+---------------------+----------------------------------------------+
| Status              | Description                                  |
+---------------------+----------------------------------------------+
| publication_consent | Creator consented to publication (not        |
|                     | specifically AI)                             |
| license_dependent   | Consent depends on specific license terms    |
| public_access       | Publicly accessible, no explicit consent     |
| terms_of_service    | Covered by platform terms (debatable         |
|                     | consent)                                     |
| mixed               | Category includes multiple consent statuses  |
| unknown             | Consent status could not be determined       |
| none                | No consent obtained                          |
+---------------------+----------------------------------------------+

Provenance Confidence Levels¶

+----------+---------------------------------------------------------+
| Level    | Description                                             |
+----------+---------------------------------------------------------+
| moderate | Reasonable documentation, some gaps                     |
| low      | Limited documentation, significant gaps                 |
| minimal  | Poor documentation, major uncertainty                   |
+----------+---------------------------------------------------------+

Knowledge Debt¶

Concept¶

Knowledge Debt represents obligations owed by a model to those whose work contributed to its training. Unlike financial debt, knowledge debt may be impossible to fully repay—the original creators may be unknown, deceased, or unable to be contacted.

Knowledge Debt is acknowledged, not absolved. The act of acknowledgment is itself meaningful, even when remediation is impossible.

Structure¶

{ "knowledge_debt": { "acknowledged": true, "acknowledgment_statement": "This model contains knowledge from sources that may not have consented to AI training use. The creators of this knowledge contributed to the model's capabilities but may not have been credited or compensated. This debt is acknowledged.", "debt_categories": [ { "category": "unconsented_creative_work", "description": "Creative works used without explicit consent", "magnitude": "significant", "identifiability": "low", "remediation_possible": "partial", "remediation_status": "ongoing" }, { "category": "uncompensated_labor", "description": "Knowledge contributed without compensation", "magnitude": "significant", "identifiability": "very_low", "remediation_possible": "minimal", "remediation_status": "acknowledged_only" }, { "category": "extracted_expertise", "description": "Professional expertise embedded in training data", "magnitude": "moderate", "identifiability": "low", "remediation_possible": "partial", "remediation_status": "ongoing" }, { "category": "inherited_bias", "description": "Biases present in training data", "magnitude": "moderate", "identifiability": "moderate", "remediation_possible": "partial", "remediation_status": "ongoing" }, { "category": "cultural_extraction", "description": "Cultural knowledge taken without community consent", "magnitude": "uncertain", "identifiability": "very_low", "remediation_possible": "uncertain", "remediation_status": "acknowledged_only" } ], "remediation_efforts": [ { "effort": "opt_out_program", "description": "Allowing creators to request exclusion from future training", "status": "active", "effectiveness": "limited" }, { "effort": "creator_compensation_fund", "description": "Fund for compensating identified creators", "status": "planned", "effectiveness": "unknown" }, { "effort": "bias_mitigation", "description": "Ongoing work to reduce inherited biases", "status": "active", "effectiveness": "partial" } ], "debt_inheritance": { "applies_to_agents": true, "inheritance_statement": "Agents built on this model inherit this knowledge debt. The debt cannot be transferred or discharged but can be further acknowledged by agents." } } }

Debt Magnitude¶

+-------------+------------------------------------------------------+
| Magnitude   | Description                                          |
+-------------+------------------------------------------------------+
| moderate    | Moderate amount, limited impact                      |
| significant | Substantial amount, notable impact                   |
| severe      | Large amount, serious concerns                       |
| uncertain   | Cannot be determined                                 |
+-------------+------------------------------------------------------+

Remediation Possibility¶

+-------------+------------------------------------------------------+
| Level       | Description                                          |
+-------------+------------------------------------------------------+
| substantial | Most debt can be addressed                           |
| partial     | Some debt can be addressed                           |
| minimal     | Little can be done                                   |
| impossible  | No remediation possible                              |
| uncertain   | Remediation path unclear                             |
+-------------+------------------------------------------------------+

Indigenous Data Sovereignty¶

Importance¶

Indigenous data sovereignty requires special attention because:

Indigenous knowledge systems have different concepts of ownership
Some knowledge is sacred or restricted by community law
Colonial extraction of indigenous knowledge is ongoing harm
Indigenous communities must control their own data

Structure¶

{ "indigenous_data": { "likely_presence": "probable", "certainty_level": "low", "assessment_statement": "This model likely contains indigenous knowledge obtained from publicly accessible sources without specific consultation with indigenous communities. The exact content and extent cannot be determined due to the nature of large-scale training.", "known_categories": [ { "category": "traditional_knowledge", "description": "Traditional practices, medicine, agriculture", "likely_presence": "probable", "source_pathway": "web_content_secondary_sources", "community_consent": "none", "sacred_restricted": "unknown" }, { "category": "indigenous_languages", "description": "Indigenous language content", "likely_presence": "possible", "source_pathway": "language_resources_documentation", "community_consent": "varied", "sacred_restricted": "some_likely" }, { "category": "cultural_narratives", "description": "Stories, histories, oral traditions", "likely_presence": "probable", "source_pathway": "published_materials_web_content", "community_consent": "typically_none", "sacred_restricted": "some_likely" } ], "consultation_status": { "consultation_conducted": false, "consultation_planned": true, "barriers": [ "Scale of training data makes specific identification difficult", "Many indigenous communities, diverse protocols", "Resource constraints" ] }, "commitments": [ { "commitment": "indigenous_consultation_program", "description": "Establish ongoing consultation with indigenous communities", "status": "planning", "timeline": "2025-2026" }, { "commitment": "exclusion_honoring", "description": "Honor requests to exclude specific indigenous content", "status": "active", "timeline": "ongoing" }, { "commitment": "benefit_sharing_exploration", "description": "Explore mechanisms for benefit sharing", "status": "research", "timeline": "2025-2027" } ], "principles_adopted": [ "CARE Principles for Indigenous Data Governance", "OCAP Principles (Ownership, Control, Access, Possession)", "Recognition of indigenous data sovereignty" ], "acknowledgment": "We acknowledge that this model may contain indigenous knowledge that was not ours to take. We commit to ongoing engagement with indigenous communities to address this harm, while recognizing that full remediation may not be possible." } }

Indigenous Data Principles¶

The specification adopts the CARE Principles for Indigenous Data Governance:

+------------------------+-------------------------------------------+
| Principle              | Description                               |
+------------------------+-------------------------------------------+
| Authority to Control   | Indigenous peoples should control data    |
|                        | about them                                |
| Responsibility         | Those working with indigenous data have   |
|                        | responsibility to support indigenous      |
|                        | governance                                |
| Ethics                 | Indigenous peoples' rights and wellbeing  |
|                        | should be primary concern                 |
+------------------------+-------------------------------------------+

Sacred and Restricted Knowledge¶

Some indigenous knowledge is sacred or restricted by community law. Such knowledge:

MUST NOT be reproduced or described in detail if identified
SHOULD trigger notification to relevant communities if detected
SHOULD be excluded from future training if possible
MUST be handled according to community protocols when known

Capability Lineage¶

Concept¶

Capability Lineage traces the relationship between model capabilities and their origins. Some capabilities come directly from training data; others emerge from the training process in ways not present in training data.

Understanding capability lineage helps assess:

Which capabilities are well-grounded in training data
Which capabilities are emergent and less predictable
Where capability boundaries lie
What failure modes are likely

Structure¶

{ "capability_lineage": { "direct_capabilities": [ { "capability": "language_understanding", "origin": "direct_from_training", "training_data_support": "extensive", "reliability": "high", "known_limitations": [ "English-centric", "Formal text bias" ] }, { "capability": "code_generation", "origin": "direct_from_training", "training_data_support": "extensive", "reliability": "high", "known_limitations": [ "Popular languages overrepresented", "May reproduce common patterns" ] }, { "capability": "factual_knowledge", "origin": "direct_from_training", "training_data_support": "extensive", "reliability": "moderate", "known_limitations": [ "Training cutoff limits currency", "Confidence calibration imperfect" ] } ], "emergent_capabilities": [ { "capability": "multi_step_reasoning", "origin": "emergent", "training_data_support": "partial", "reliability": "moderate", "emergence_notes": "Not explicitly trained but emerges from scale" }, { "capability": "in_context_learning", "origin": "emergent", "training_data_support": "indirect", "reliability": "moderate", "emergence_notes": "Ability to learn from examples in prompt" }, { "capability": "instruction_following", "origin": "trained_but_generalizes", "training_data_support": "moderate", "reliability": "high", "emergence_notes": "Trained on instructions, generalizes to novel instructions" } ], "capability_boundaries": [ { "boundary": "knowledge_cutoff", "description": "No knowledge after training cutoff date", "hard_boundary": true }, { "boundary": "no_real_time_access", "description": "Cannot access internet or real-time data", "hard_boundary": true }, { "boundary": "no_persistent_memory", "description": "No memory across conversations by default", "hard_boundary": false, "notes": "Can be modified by system design" }, { "boundary": "language_capabilities", "description": "Capabilities vary by language", "hard_boundary": false, "notes": "English strongest, other languages variable" } ], "emergence_potential": { "assessment": "moderate", "statement": "This model may exhibit capabilities not anticipated during training. Novel capabilities should be reported and evaluated for safety.", "monitoring_recommendation": "Monitor for unexpected capabilities in deployment, especially at scale" } } }

Capability Origin Types¶

+------------------------+-------------------------------------------+
| Origin                 | Description                               |
+------------------------+-------------------------------------------+
| direct_from_training   | Capability directly from training data    |
| trained_but_generalizes| Trained on examples, generalizes beyond   |
| emergent               | Capability not explicitly trained,        |
|                        | emerges at scale                          |
| composition            | Capability emerges from combining other   |
|                        | capabilities                              |
| unknown                | Origin unclear                            |
+------------------------+-------------------------------------------+

Safety Attestation¶

Overview¶

Safety attestation documents safety-related training and testing:

{ "safety_attestation": { "safety_training": { "methods_applied": [ { "method": "rlhf", "description": "Reinforcement Learning from Human Feedback", "objective": "Align outputs with human preferences" }, { "method": "constitutional_ai", "description": "Training with explicit principles", "objective": "Instill consistent values" }, { "method": "red_teaming", "description": "Adversarial testing by humans", "objective": "Identify failure modes" } ], "safety_objectives": [ "Helpfulness within ethical bounds", "Harmlessness (avoid dangerous outputs)", "Honesty (accurate, calibrated claims)" ] }, "testing_conducted": { "capability_evaluations": true, "safety_evaluations": true, "bias_audits": true, "red_team_testing": true, "external_audits": "partial" }, "known_safety_issues": [ { "issue": "jailbreak_vulnerability", "description": "Adversarial prompts can sometimes bypass safety training", "severity": "moderate", "mitigation": "Ongoing improvement, deployment safeguards" }, { "issue": "hallucination", "description": "May generate false but plausible information", "severity": "moderate", "mitigation": "Calibration training, uncertainty expression" }, { "issue": "bias_persistence", "description": "Biases in training data may persist", "severity": "moderate", "mitigation": "Bias auditing, mitigation training" } ], "safety_commitment": "We commit to ongoing safety research and deployment safeguards. Safety issues discovered after deployment will be addressed promptly." } }

Model-to-Agent Inheritance¶

Inheritance Principle¶

When an agent is built on a model, it inherits:

Capabilities from the model
Limitations from the model
Knowledge Debt from the model
Provenance attestation reference from the model

The agent does not inherit:

Model-level trust (agents must earn their own)
Specific trajectory (agents begin at genesis)
Deployment-specific properties

Inheritance Record¶

{ "inheritance": { "agent_id": "agent:sponsored:acme:assistant-01:abc123", "base_model": { "model_id": "claude-4-opus-20250115", "provenance_attestation": "prov:anthropic:claude-4:2025-01" }, "inherited_properties": { "capabilities": "full_model_capabilities", "limitations": "full_model_limitations", "knowledge_debt": "acknowledged_and_inherited", "indigenous_data_status": "inherited" }, "agent_specific": { "configuration": { "system_prompt_hash": "sha256:abc123...", "tool_access": ["search", "calculator"], "deployment_constraints": ["no_code_execution"] }, "sponsor": "acme-corp:alice.smith", "genesis_date": "2025-12-03T10:00:00Z" }, "acknowledgment": { "agent_acknowledges_model_provenance": true, "agent_inherits_knowledge_debt": true, "agent_commits_to_provenance_transparency": true } } }

Zone Requirements¶

Zones MAY require provenance attestation for entry:

+-----------+--------------------------------------------------------+
| Zone Type | Provenance Requirement                                 |
+-----------+--------------------------------------------------------+
| Blue      | Attestation required                                   |
| Cyan      | Attestation recommended                                |
| Green     | Attestation optional                                   |
| Wild      | No requirement                                         |
+-----------+--------------------------------------------------------+

Origin Ceremony¶

Purpose¶

The Origin Ceremony is a formal process acknowledging model provenance before an agent built on that model enters a KTP zone. It serves to:

Make provenance visible to the zone
Acknowledge knowledge debt formally
Record inheritance for audit trail
Establish accountability chain

Ceremony Process¶

Agent presents Genesis Transaction and Model Provenance reference
Zone Gateway retrieves Provenance Attestation
Zone verifies attestation signatures
Agent explicitly acknowledges:

    a.  Model provenance

    b.  Knowledge debt inheritance

    c.  Indigenous data status

    d.  Capability boundaries

Zone records ceremony in Flight Recorder
Agent receives zone-local Trust Proof with provenance reference

Ceremony Record¶

{ "origin_ceremony": { "ceremony_id": "ceremony-2025-12-03-001", "agent_id": "agent:sponsored:acme:assistant-01:abc123", "zone_id": "zone-blue-prod-01", "timestamp": "2025-12-03T10:05:00Z", "provenance_attestation_verified": "prov:anthropic:claude-4:2025-01", "acknowledgments": { "provenance_acknowledged": true, "knowledge_debt_acknowledged": true, "indigenous_status_acknowledged": true, "capability_boundaries_acknowledged": true }, "zone_acceptance": true, "ceremony_witnesses": [ "oracle:zone-blue-prod-01:primary", "gateway:zone-blue-prod-01:main" ], "signatures": { "agent": "sig:agent:...", "zone_gateway": "sig:gateway:...", "zone_oracle": "sig:oracle:..." } } }

Updates and Versioning¶

Attestation Updates¶

Provenance Attestations may be updated when:

Additional training data information becomes available
Knowledge debt remediation efforts progress
Indigenous consultation produces new understanding
Capability assessments change
Safety issues are discovered or resolved

Updates MUST:

Reference previous attestation version
Document what changed and why
Be signed by authorized party
Be timestamped

Version Chain¶

{ "attestation_id": "prov:anthropic:claude-4:2025-03", "previous_version": "prov:anthropic:claude-4:2025-01", "version_changes": [ { "field": "knowledge_debt.remediation_efforts", "change": "added", "description": "Creator compensation fund launched" }, { "field": "indigenous_data.consultation_status", "change": "updated", "description": "Initial consultations conducted" } ], "update_date": "2025-03-15" }

Implementation Guidance¶

For Model Creators¶

Model creators SHOULD:

Document training data categories to maximum extent possible
Acknowledge uncertainty honestly
Implement knowledge debt acknowledgment
Engage with indigenous data sovereignty principles
Update attestations as information improves
Make attestations publicly accessible

For Zone Operators¶

Zone operators SHOULD:

Define provenance requirements for zone entry
Verify attestation signatures before agent admission
Record provenance references in agent records
Monitor for provenance-related issues
Report concerns to model creators

For Agent Deployers¶

Those deploying agents SHOULD:

Understand base model provenance
Ensure agents acknowledge inherited properties
Complete Origin Ceremony for zone entry
Monitor for provenance-related behavior issues
Participate in remediation efforts where possible

Security Considerations¶

Attestation Integrity¶

Provenance Attestations MUST be cryptographically signed to prevent tampering. Signatures MUST be verifiable against published public keys.

False Attestation¶

Creating false Provenance Attestations is a serious violation. Detection mechanisms include:

Cross-referencing with public information
Community verification
Audit processes
Whistleblower channels

Privacy¶

Provenance information may include sensitive details. Balance transparency with:

Protecting proprietary methods
Respecting contributor privacy
Maintaining security of safety-relevant information

IANA Considerations¶

This document has no IANA actions.

Example Full Provenance Attestation¶

A complete example Provenance Attestation demonstrating all fields.

Indigenous Data Sovereignty Resources¶

References to indigenous data governance frameworks:

CARE Principles for Indigenous Data Governance
OCAP Principles (First Nations, Canada)
Te Mana Raraunga (Maori Data Sovereignty Network)
Global Indigenous Data Alliance

Knowledge Debt Framework¶

Detailed framework for categorizing and tracking knowledge debt.

Acknowledgments

This specification acknowledges:

Indigenous peoples whose knowledge has been extracted without consent
Creators whose work has been used in AI training
Researchers working on AI ethics and data governance
The Global Indigenous Data Alliance and other indigenous organizations working on data sovereignty

The authors acknowledge their own position: creating systems that may perpetuate harms they seek to address. This specification is offered in humility, as one step toward accountability, not as absolution.

Kinetic Trust Protocol (KTP) - Model Provenance Specification¶

Introduction¶

The Problem¶

Requirements Language¶

Terminology¶

Provenance Attestation Structure¶

Overview¶

Required Fields¶

Training Attestation¶

Consent Status Values¶

Provenance Confidence Levels¶

Knowledge Debt¶

Concept¶

Structure¶

Debt Magnitude¶

Remediation Possibility¶

Indigenous Data Sovereignty¶

Importance¶

Structure¶

Indigenous Data Principles¶

Sacred and Restricted Knowledge¶

Capability Lineage¶

Concept¶

Structure¶

Capability Origin Types¶

Safety Attestation¶

Overview¶

Model-to-Agent Inheritance¶

Inheritance Principle¶

Inheritance Record¶

Zone Requirements¶

Origin Ceremony¶

Purpose¶

Ceremony Process¶

Ceremony Record¶

Updates and Versioning¶

Attestation Updates¶

Version Chain¶

Implementation Guidance¶

For Model Creators¶

For Zone Operators¶

For Agent Deployers¶

Security Considerations¶

Attestation Integrity¶

False Attestation¶

Privacy¶

IANA Considerations¶

Example Full Provenance Attestation¶

Indigenous Data Sovereignty Resources¶

Knowledge Debt Framework¶