08. Phylogenetic Audit

Kullback-Leibler (KL) Divergence & Model Phylogeny

This module treats the evolution of an AI model as a digital phylogeny—a lineage of state transitions. Utilizing Kullback-Leibler (KL) Divergence, the audit measures the informational distance between a model’s current probability distribution and its sovereign baseline. This ensures that even as the model learns and 'mutates' over time, its evolutionary trajectory remains within governed parameters and its intellectual lineage remains verifiable. 

Quantifying Algorithmic Mutation

Institutional assets are protected through the quantification of 'algorithmic mutation.' By setting a maximum threshold for KL Divergence, this module identifies when a model has drifted too far from its original mission-critical logic. This provides a mathematical 'stop-loss' for AI behavior, ensuring that the model never evolves into a maladaptive state that could jeopardize institutional capital or reputation. 

Biological Property Principles Applied to AI Weights

Building directly on my foundational Columbia University research into DNA sovereignty, this module establishes that a model’s weights are its 'Neural DNA.' Just as genetic markers prove biological parentage, the Phylogenetic Audit proves the intellectual origin of a model. This ensures that an institution’s proprietary IP remains legally defensible, even after years of continuous training and environmental adaptation. 

Lineage Consistency & Version Forensics

To provide a complete audit record, the suite logs every 'branch' in the model’s evolutionary tree. By tracking the manifold changes across different versions, we create a transparent map of how the AI has grown. Every significant shift in the KL Divergence score is recorded as an immutable forensic entry, allowing auditors to trace the model’s current behavior back to its founding data and steering commands. 

Divergence Metrics & Lineage Source Code

The Python implementation of the KL Divergence auditing script and the evolutionary mapping tools are available for peer review on our GitHub repository. This documentation provides the mathematical proof required to unify biological lineage theory with artificial neural governance, establishing a new global standard for AI forensics. 

By mapping the evolutionary trajectory of AI, the Phylogenetic Audit provides the ultimate forensic record of neural development. This concludes the NEnterprise Forensic Suite, ensuring total governance from data entry to architectural evolution.