Open Data Infrastructure Control Planes for AI Workloads

AI workloads do not need another dashboard pretending to be governance. They need a control plane that sits in the path of data use.

AI needs a control plane

An AI workload can discover data, retrieve context, call tools, run queries, propose changes, and trigger business workflows. Each action touches a different slice of infrastructure. Without a control plane, teams end up governing the model in one place, data access in another, and evaluation somewhere else.

Open Data Infrastructure gives the control plane its parts: catalogs, policy engines, metadata, lineage, query services, data product contracts, access logs, and evaluation traces. The hard part is making those parts behave like one operating system for data use.

The plane is made of evidence

The control plane should answer practical questions. Which data products can this agent use? Which policy decision allowed access? Which source and freshness state shaped the answer? Which tool ran? Which evaluation result says the behavior is acceptable?

NIST AI RMF supplies risk-management framing. OpenLineage and W3C PROV supply provenance and lineage building blocks. The ODI contribution is connecting those building blocks to the actual data infrastructure that AI workloads use.

The ODI architecture pattern

A practical architecture starts with the catalog as the control anchor. Policy engines enforce allowed use. Metadata describes meaning and ownership. Lineage tracks source and transformation paths. Query services expose governed access. Evaluation traces show whether the workload still behaves.

For adjacent context, read the ODI control plane, the ODI reference architecture for agents, and AI-ready entitlement graphs.

What breaks first

• Catalogs describe data but do not enforce or record agent access decisions.
• Policy engines approve access without passing useful context to evaluation and audit systems.
• Lineage stops at data pipelines and misses tool calls.
• Evaluation traces store prompts and outputs but not the data product evidence behind them.

Questions to ask

Ask which system owns the control decision, which systems produce evidence, and which identifier connects the whole path. Ask whether a failed AI workflow can be traced from answer back to source data, policy, and owner.

AI governance becomes real when the control plane can stop, explain, and replay the data path.

Sources to start with

These primary sources anchor the technical claims in this guide.

• NIST AI Risk Management Framework
• OpenLineage object model documentation
• OpenLineage facets documentation
• W3C PROV overview

The control plane is not a layer you buy. It is the evidence your architecture can produce.