02. Forensic Chain

Perceptron-Based Lineage Tracking

The Forensic Chain serves as the definitive record of 'Neural DNA' provenance. Utilizing a Perceptron-based auditing logic, this module assigns a unique cryptographic identifier to every weight update and training iteration. By establishing an immutable chain of custody, the module ensures that the model’s intellectual lineage is documented and protected from unauthorized synthetic overrides or state-injection attacks. 

Forensic Weight Validation

Institutional governance requires absolute certainty regarding the origin and training history of deployed models. This module aligns with these requirements by providing a forensic audit trail that validates every change in the neural architecture. This transparency ensures that the model remains compliant with high-level regulatory standards, preventing the ingestion of untraceable or proprietary-infringing data packets. 

Neural State-Space Governance

Building upon my Columbia University research into information sovereignty, the Forensic Chain treats a model's state-space as a sovereign asset. It prevents the 'blurring' of intellectual property by explicitly mapping the evolution of weights over time. This ensures that the proprietary 'genome' of the institution’s AI remains forensically distinct and legally defensible. 

Hash-Based State Verification

To maintain the absolute stability of the audit trail, this module executes automated background integrity checks. By performing periodic hash validations across distributed architectures, the system ensures the forensic chain remains unbroken and consistent. Any detected deviation in the neural state triggers an immediate governance alert, preventing compromised data from advancing through the suite. 

Immutable Provenance Source Code

The technical architecture for the immutable logging protocol is documented for independent peer review. Access the Python implementation on the GitHub repository to verify the cryptographic methods used to secure weight provenance. Transparent documentation is a core pillar of my framework, ensuring that the 'Neural DNA' of every model is protected by verifiable, research-backed code. 

Once data custody is cryptographically secured, the module generates a unique hash signature for the specific neural state. This verified state then advances to the 03. Attractor Safeguard to establish mathematical operational bounds.