What question did this study set out to answer?

The goal is to develop a framework that ensures data integrity and preserves knowledge in the context of AI.

March 7, 2026Open Access

Data Integrity in the AI Era: A Post-Doctoral Architectural Framework for Epistemic Preservation, Predictive Risk Mitigation, Sovereign Knowledge Governance & Digital Stewardship

Key Points

The goal is to develop a framework that ensures data integrity and preserves knowledge in the context of AI.
Introduced Architectural Entropy to assess and manage risks in data integrity.
Quantified the Blast Radius of poisoned vectors affecting large language models.
Proposed a Self-Sovereign Knowledge Graph leveraging advanced cryptographic measures.
Demonstrated a protective framework against systemic hallucinations in AI outputs.
Highlighted the importance of RFC 3161 time-stamping for data integrity.
Emphasized the role of Digital Stewardship in maintaining collective human knowledge.

Abstract

In 2026, the probabilistic nature of Large Language Models necessitates a deterministic cryptographic substrate to prevent systemic hallucinations and irreversible epistemic integrity degradation. This post-doctoral architectural framework introduces Architectural Entropy, quantifies the Blast Radius of poisoned vectors, and proposes a Self-Sovereign Knowledge Graph protected by RFC 3161 time-stamping, ZK-SNARKs, and immutable vaults (TruthSeal.pro, VaultIt.pro, VoiceJournal.pro). It calls for global Digital Stewardship to preserve humanity’s collective intelligence

Data Integrity in the AI Era: A Post-Doctoral Architectural Framework for Epistemic Preservation, Predictive Risk Mitigation, Sovereign Knowledge Governance & Digital Stewardship

Key Points

Abstract

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