AI data governance
AI data governance keeps protection attached to the data, so the rules follow every record an agent touches.
In an agentic AI system, data moves through models, tools, and services the original writer never chose. Conventional stacks put confidentiality and authorization in storage, so once a record is copied out of the application, the protections the writer set do not travel with it. AI data governance seals protection into the record itself. Fields are encrypted where they are created, routed to the readers authorized to see them, and bound to a signed identity and a stated policy, so the governance stays with the data rather than with the store that happens to hold it. Cyaxios builds that layer on our TN-Proto research.
Explore TN-ProtoWhy it has to change for AI
A record outlives the reach of the system that wrote it.
Application data carries account identifiers, payment attributes, model inputs, and internal decisions, and different parties need different parts of it. Once the record leaves the application, transport security ends at the next hop, storage encryption protects the disk rather than the reader, and access control belongs to whichever service currently holds the copy. The data stays useful. Its protection does not follow it.
Agentic systems make this sharper, because agents pass records between tools and services at machine speed, and each hop is a place where the original intent can be lost. AI data governance answers this by moving protection to the point where the data is created, so it holds wherever the record goes.
What it covers
Governance that travels: confidentiality, policy, and evidence in every record.
Field-level confidentiality
Only what a reader may see
Sensitive fields are encrypted at creation and routed to authorized readers, so a copied record exposes only what a given reader is permitted to see.
Portable policy
Rules that ride with the data
The publisher's rules are welded to the record and echoed with it, so a record's declared governance travels with the data instead of living in a separate system.
Verifiable evidence
Origin, order, and audit
Each record is signed and chained, so a reader can verify origin and order, and an auditor can follow a decision to the data and the policy behind it.
A framework you can adopt
A data governance framework built for agentic systems.
Adopting AI data governance is a matter of strategy before technology. We help you decide which fields carry risk, who is authorized to read them, and what evidence a reviewer needs, then put the controls where the data is produced.
The result is a data governance strategy that a risk, compliance, or audit function can stand behind, and that keeps working as records move through agents, tools, and downstream services.
In the governance program
Where AI data governance meets agent decisions and audit trails.
AI data governance is the data layer of a wider governance program. It pairs with the decision governance in our AI governance work, the extraction protections in our AI agent security work, and the reviewable evidence in our audit trail work, all built on the TN-Proto protocol.
FAQ
Common questions.
What is AI data governance?
It keeps confidentiality, authorization, and evidence attached to each record, so protection travels with the data through the models, agents, tools, and services that use it, rather than depending on the store that happens to hold it.
How is AI data governance different from data governance?
Conventional data governance often centers on master data, catalogs, and quality in a central store. AI data governance keeps field-level protection, policy, and audit evidence bound to the record itself as it moves through agentic systems.
What is a data governance framework for AI?
It is the set of decisions about which fields carry risk, who may read them, and what evidence a reviewer needs, put into controls at the point where the data is created.
How does this support audit and compliance?
Signed, chained records let a reviewer verify origin and order and trace a decision to the data and the policy behind it.
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