Regulated Industry Model Governance With Provenance

The regulatory gap in model training

The Federal Reserve's SR 11-7 guidance on model risk management requires that financial institutions understand and document the data, assumptions, and limitations of every model they use. The EU AI Act requires documentation of training data, training methodology, and validation procedures for high-risk AI systems. FDA guidance for AI in medical devices requires traceability from training data to model behavior.

Current training practices produce models that cannot satisfy these requirements. A foundation model trained on internet-scale data cannot document every training example. A fine-tuned model inherits the provenance gap of its base model. The model exists, and it performs, but the chain from training data to model behavior is opaque.

Why model cards and data sheets are insufficient

Model cards describe a model's intended use, performance characteristics, and known limitations. Datasheets for datasets describe training data composition and collection methodology. Both are documentation artifacts, not structural properties of the model. A model card can be inaccurate. A datasheet can be incomplete. Neither provides verifiable provenance that a regulator can independently audit.

The documentation describes the model. It does not structurally connect the model to its training lineage. A regulator reviewing a model card must trust that the card accurately describes the model. There is no mechanism for independent verification.

How training governance addresses this

Training governance maintains structural provenance through the entire training process. Each training example's contribution is tracked: which data entered the training loop, what governance profile it carried, which layers received gradient updates, and what the impact on model behavior was.

Entropy-depth profiles provide a structural characterization of how training data influenced the model at each layer. A regulator can examine the entropy-depth profile to understand whether the model's behavior in a specific domain is driven by deep integration of appropriate training data or by shallow pattern matching on potentially inappropriate data.

The governed training loop ensures that every training step complies with governance constraints. Data that should not be used for training in a specific domain is excluded structurally, not by filtering. Fine-tuning provenance tracks exactly what data was used for each fine-tuning run, enabling the institution to demonstrate that fine-tuning did not introduce data that violates regulatory requirements.

Knowledge retention monitoring detects when training causes the model to lose capability in previously validated domains, a common failure mode in fine-tuning. The governance layer can prevent training steps that would degrade validated capabilities, maintaining the model's certified performance characteristics.

What implementation looks like

A regulated institution deploying training governance wraps its training pipeline with a provenance layer that records every training step, every data contribution, and every governance constraint evaluation. The resulting model carries a verifiable training lineage that regulators can audit independently.

For pharmaceutical companies training models for drug discovery, training governance provides the data lineage documentation that FDA submission requires. Every training example's source, rights status, and integration depth is recorded and verifiable.

For financial institutions, training governance satisfies SR 11-7 model risk management requirements by providing the training documentation, validation evidence, and ongoing monitoring infrastructure that regulators expect, backed by structural provenance rather than self-reported documentation.