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    AI Data Governance: Governing the Data Behind Your Models

    AI data governance is where data governance meets the specific demands of artificial intelligence. Classic data governance asks who can access what data and whether it is accurate. AI adds harder questions: what data trained this model, can you trace a model output back to its sources, and does sensitive data stay inside your control when a model processes it. For regulated institutions, getting this right is a prerequisite for trustworthy AI, not an afterthought.

    What is AI data governance?

    AI data governance is the set of policies and controls that manage the data flowing into and out of AI systems: the data used to train or tune models, the data fed to them at inference, and the outputs they produce. It extends traditional data governance to cover the parts of the data lifecycle that AI introduces, where data becomes model behavior and model behavior becomes a business action.

    Why it is different from traditional data governance

    Three things make AI data governance harder than the classic discipline:

    • Training data is consequential. What a model learned from shapes everything it does later, so the provenance and rights of training data matter in a way that a static database record does not.
    • Lineage is harder to trace. Once data is absorbed into a model, connecting an output back to its sources requires deliberate design. Lineage cannot be reconstructed after the fact.
    • Data moves through third parties. Many AI workflows send data to external model providers. Without control, sensitive data can leave your boundary in ways traditional governance never contemplated.

    The core controls

    A workable AI data governance program covers a few essentials: provenance and rights for training and tuning data; lineage that connects model inputs and outputs to their sources; residency and access control so sensitive data stays where it is allowed to be; and retention rules for prompts, outputs, and logs. The theme across all of them is the same: keep the data, and the record of how it was used, inside a boundary you control.

    Where AI data governance meets AI governance

    Data is one of three things you have to govern together: the data, the model, and the action it takes. Governing data in isolation leaves gaps, because the risk shows up when a model uses that data to do something. This is why AI data governance works best as part of a single control and assurance layer that also governs models and agent actions, and produces one connected record across all three. That is the approach behind the AI governance platform, and it is what keeps the evidence regulator-ready rather than scattered.

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    REIGN FAQ

    Frequently asked questions

    What is AI data governance?
    The policies and controls that manage data flowing into and out of AI systems: training and tuning data, inference inputs, and model outputs. It extends classic data governance to cover where data becomes model behavior.
    How is AI data governance different from data governance?
    It adds the AI-specific parts: the provenance and rights of training data, lineage from outputs back to sources, and control over data that moves to external model providers. These are not concerns in traditional, static data governance.
    What are the core AI data governance controls?
    Provenance and rights for training data, lineage between inputs and outputs, residency and access control, and retention rules for prompts, outputs, and logs. The common goal is keeping data and its usage record inside a boundary you control.
    Does AI data governance require keeping data inside your own environment?
    For regulated institutions, usually yes. Keeping data and the control point inside your trust boundary is the most reliable way to ensure sensitive data does not leave your control when a model processes it.