What question did this study set out to answer?

The aim is to improve governance in generative AI by implementing a zero-trust architecture that enhances security and integrity.

March 2, 2026Open Access

Mnemosyne Protocol v1.7.1: Deterministic Governance and Zero-Trust Pipelines

Key Points

The aim is to improve governance in generative AI by implementing a zero-trust architecture that enhances security and integrity.
Developed an auditable Policy-as-Code layer to ensure transparency and compliance.
Employed Merkle Tree constructs for visual compliance and cryptographic security.
Transformed AI workflows into governed units under human authority.
Established a fail-closed architecture to mitigate risks in enterprise workflows.
Provided increased IP protection and supply-chain integrity through structured governance.
Optimized the performance of AI swarms as controlled labor units.

Abstract

The rapid adoption of Generative AI has introduced ungoverned probabilistic generation risks to enterprise workflows. Current paradigms operating as untrusted "black boxes" fail to provide IP protection and supply-chain integrity. The Mnemosyne Protocol introduces a zero-trust, fail-closed architecture via an auditable Policy-as-Code layer. Visual compliance is cryptographically secured via Merkle Tree constructs, transforming AI swarms into mathematically chained, high-volume labor units under sovereign human authority. Formatting + schema hardening; no semantic changes.

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Cite This Study

Mert Kerem Salman (Fri,) studied this question.

synapsesocial.com/papers/69a52df3f1e85e5c73bf1267 https://doi.org/https://doi.org/10.5281/zenodo.18809100

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