Published June 23, 2026 | Version 2.0
Preprint Open

GlyphAI: A Tiered Framework for Semantic-Preserving Data Transformation and AI-Safe Privacy Gateways

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Description

This updated version presents GlyphAI as a tiered framework for semantic-preserving, non-reversible data transformation and privacy-oriented AI workflows. The framework supports controlled transformation of sensitive textual, numerical, and contextual data into glyph-based representations while maintaining selected semantic and audit-relevant properties.

The update introduces an expanded architecture covering Tier 1 public semantic compression, Tier 2 codebook-controlled privacy transformation, Tier 3 enterprise-grade semantic abstraction, and Tier 4 platform/ecosystem deployment. It also describes the Privacy Gateway model, in which sensitive content is transformed before interaction with external or internal AI systems, enabling safer use of large language models without directly exposing raw personal or confidential data.

Potential applications include financial records, legal case materials, HR and recruitment documents, research datasets, insurance claims, healthcare administration, and enterprise AI governance. The publication discusses audit trails, data minimisation, controlled restoration, semantic preservation, and compliance-supporting workflows aligned with privacy-by-design principles.

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Dates

Submitted
2026-02-17
This work introduces GlyphAI, a semantic framework for non‑reversible, meaning‑preserving data transformation designed to address the growing tension between large‑scale data utilization and modern security, privacy, and compliance requirements. Instead of storing raw or reconstructable data, GlyphAI converts information into compact semantic structures that retain conceptual meaning while eliminating the possibility of reconstructing the original form. This architecture provides inherent resistance to post‑quantum attacks, reduces breach value, and supports privacy‑by‑design principles. The monograph formalizes multi‑tier semantic compression, decoder‑isolated architectures, and structural data minimization as foundational mechanisms for secure AI systems. It introduces a layered semantic representation model, a semantic abstraction pipeline, and a Literature Module for controlled stylistic reconstruction when required. The framework integrates insights from linguistics, cognitive science, information theory, symbolic AI, and privacy‑enhancing technologies to propose a meaning‑centric alternative to traditional data storage and processing.
Updated
2026-06-23
Version 2.0 update: - Added expanded GlyphAI tiered architecture. - Added the Privacy Gateway model for AI-safe data workflows. - Added Simulation Mode for non-transformative risk assessment. - Added audit evidence layer and compliance-supporting workflow concepts. - Clarified codebook-controlled transformation and controlled restoration. - Expanded potential use cases across finance, legal, HR, research, insurance, healthcare administration, and enterprise AI governance.