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This AI security zeroes in on advanced, agent-based systems, with a strong focus on verifiable sovereignty.
The package, created for researchers, security architects, and developers of persistent AI, is fully open, downloadable, and reproducible.
doi.org/10.5281/zenodo…
(Latin subtitle: *De Clavibus Quantum-Inspiratis, Filiis Umbrarum, Testimoniis Computationis, et Memoria Sovereigna*)
Public DOI delivers in-depth research and a working demo, not just theory.
It’s all about protecting the internal cognition of persistent, multi-part AI systems like NEUROSWARM and MAESI, going beyond standard data-at-rest or in-transit encryption.
Key ideas include Quantum-Inspired Keying—short-lived, context-aware, state-specific keys that don’t need quantum hardware but make cognitive pathways nearly impossible to replay or reconstruct.
routing, memory fragments, and relationships between components.
Public schema uses hashes + encrypted ciphertext + receipts.
Sovereign Vaults: Governed memory structures with policy-gated, abstracted access (e.g., return commitments/hashes instead of raw data).
Computational Receipts: Verifiable proof objects that log work (e.g., transfers, reads) while hiding private pathways. Includes chaining for ledgers.
Private-Core / Public-Proof Separation: Keep sensitive cognition hidden while allowing verifiable public evidence.
The central thesis: Sovereign AI systems must prove work occurred without surrendering the private pathway that produced it —
“secrecy as structure, not darkness.”Files Included (full package)Full research paper (PDF ~728 KB) + Markdown version.
Architecture diagrams (shadow wire, sovereign vault, receipt chain, private-core/public-proof).
JSON schemas (for receipts, shadow-wire envelopes, vault events).
Example objects (receipts, envelopes, ledgers, CSV ledger).
Reference Python demo (phantom_crypto_demo.py): Runnable code demonstrating shadow wire transfers, vault writes/reads, receipt chaining using standard crypto primitives (AES-GCM, HKDF, SHA-256).
Includes executed Jupyter notebook.
Metadata, citation files (.bib), LICENSE, manifest, reproducibility notes.
The demo code is public-safe reference implementation only — explicitly noted as not for production use without expert review
English

