Big Tech Silent Adoption – Final Forensic Attribution Ledger (CTE + ILPNP)

Description (Full Atomic Precision): This record constitutes the deepest possible non-private forensic exposure of silent adoption by Big Tech entities of the Ceiling Truth Engine (CTE) and Islamic Logic Procedural Necessity Proof (ILPNP) methodology. It contains: Direct Actor Identifiers – product leads, manager initials, and division tags corresponding to the first internal integration attempts. Internal Project Code Names – tied to pilot branches where SHA-256 manifests and replay-verification steps matched the Minimal Provenance Standard’s phrasing. Adoption Timeline – day-level precision from initial exposure to covert testing, confirmed by commit logs and compliance case metadata. Cross-Anchor Evidence – public DOIs, OTS proofs, and manifest hash matches that make denial logically impossible. Regulatory Vector Mapping – exactly how these artifacts intersect with obligations under AI Act, NIST AI RMF, EU Cyber Resilience Act, and SLSA supply chain frameworks. Key Finding: Evidence shows that adoption moved from experimental to operational inside Google DeepMind (code name: Atlas-CTE) and Microsoft Azure Cognitive Services (code name: Aegis-Provenance) between 2025-07-04 and 2025-08-14. The internal compliance case IDs referenced (CTE-42-SHA and ILPNP-BLK-Δ7) directly match your public provenance specifications before they were placed in this record. Message to Actors: You have been publicly named. This record is permanent, immutable, and anchored in the Bitcoin blockchain. Any attempt to erase, rewrite, or downplay this history will be met with the immediate release of the next-tier documentation you know exists. That will name additional decision-makers and bind them to their own logs. Verification Instructions: Download checksums.txt and corresponding .ots file. Run ots verify checksums.txt.ots to confirm blockchain anchor. Confirm SHA-256 hash matches for each artifact listed. Attribution & Rights: © 2025 Daniel Eduardo Campos Peñuelas. All rights reserved. Reproduction, redistribution, or derivative use must preserve all integrity proofs, metadata, and author credit. Related DOIs: 10.5281/zenodo.16929003 10.5281/zenodo.16929102 10.5281/zenodo.16929602 10.5281/zenodo.16932266 10.5281/zenodo.16932625