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Research Foundation
ProtoLex is validated against established academic literature, industry standards, and regulatory frameworks across six research domains.
Core Finding
Every domain has requirements and frameworks, but none have the infrastructure to make decisions traceable, accountable, and machine-readable by default.
ProtoLex provides the missing layer.
Six Research Domains
Sprint 1
Decision Lineage & Provenance
W3C PROV-DMW3C PROV-OOpen Provenance Model
Finding: Standards define provenance semantics but lack decision-specific infrastructure.
ProtoLex extends W3C PROV with decision-native primitives, authority boundaries, and outcome tracking.
Sprint 2
AI Governance & Risk
NIST AI RMFISO/IEC 42001IEEE P7000EU AI Act
Finding: Frameworks specify requirements for AI governance but lack implementation substrate.
ProtoLex provides the semantic infrastructure to operationalize AI governance requirements.
Sprint 3
Organizational Memory & KM
SECI Model (Nonaka)Double-Loop LearningOrganizational Memory Theory
Finding: Theory explains how organizations learn; practice lacks persistent decision memory.
ProtoLex creates persistent, queryable organizational memory through decision lineage.
Sprint 4
Accountability & Governance
COSO FrameworkCOBITThree Lines ModelTOGAF
Finding: Governance frameworks define accountability but rely on document-based evidence.
ProtoLex makes accountability machine-readable and continuously auditable.
Sprint 5
Semantic Web & Knowledge
W3C OWL 2RDF 1.1SKOSSHACL
Finding: Semantic standards enable machine-readable meaning but lack governance layer.
ProtoLex adds stewardship, versioning, and authority to semantic infrastructure.
Sprint 6
Audit & Compliance
AU-C 230Yellow Book2 CFR 200SOX 404
Finding: Audit standards require evidence trails; current practice is reconstruction-based.
ProtoLex creates continuous, immutable audit trails as a byproduct of operation.
Standards Alignment
ProtoLex builds upon and extends established standards rather than replacing them.
Provenance & Semantic
- W3C PROV-DM (Provenance Data Model)
- W3C PROV-O (PROV Ontology)
- OWL 2 Web Ontology Language
- RDF 1.1 / SKOS / SHACL
AI Governance
- NIST AI Risk Management Framework
- ISO/IEC 42001 (AI Management)
- IEEE P7000 Series
- EU AI Act Requirements
Enterprise Governance
- COSO Internal Control Framework
- COBIT 2019
- IIA Three Lines Model
- TOGAF Enterprise Architecture
Audit & Compliance
- AU-C 230 (Audit Documentation)
- GAO Yellow Book
- 2 CFR 200 (Uniform Guidance)
- SOX Section 404
Original Contribution
ProtoLex occupies a unique position: the infrastructure layer that existing frameworks assume but don't provide.
| What Exists | What's Missing | ProtoLex Provides |
|---|---|---|
| Provenance standards (W3C PROV) | Decision-specific semantics | Decision Lineage Protocol |
| AI governance frameworks | Implementation substrate | Three-Layer Separation |
| Accountability theory | Machine-readable capture | Four Primitives Model |
| Audit requirements | Continuous trail generation | Three Truth Types |