Your governance stops at the file. AI doesn't.
Kynexa extends your data governance to inference. Asset- and element-level metadata, five-tier sensitivity, semantic labels and lineage: applied to retrieval and reasoning, not just storage.
- Governance built for files, not inference
- Catalogs can't govern conclusions
- An audit gap over AI
The problem you own
Your governance program was built for files, tables, columns and rows. AI operates a layer above that: it combines and reasons across sources, so sensitive meaning can emerge that no row-level control anticipated. The catalog tells you what data exists; it doesn't govern what the AI concludes from it. That gap is now your exposure — and your audit problem.
What you get
A semantic metadata catalog
Across structured and unstructured sources, built automatically and kept at source.
Multi-dimensional classification
Five-tier sensitivity (Public → Regulated), semantic labels, taxonomy and custom tagging at asset and element level.
End-to-end lineage for every AI output
Trace each answer to its governed sources for explainability and audit.
One governance model for humans and AI
So you don't maintain a separate, parallel regime for every copilot and agent.
A global manufacturer, governing AI across six systems.
A global manufacturer gained end-to-end lineage and audit across six systems: every AI output traceable to its governed sources.
What changes for you
- What you can control
- The context AI retrieves, combines, remembers and uses.
- What you can prove
- Lineage from each AI output back to governed sources.
- What changes operationally
- Governance extends from data at rest to inference.
- Primary stakeholders
- CDO, data governance, AI platform.
- Evidence produced
- Semantic metadata, classification and lineage.
See Kynexa govern your AI — in 30 minutes.
Bring a real use case. We'll set up governed retrieval, reasoning and audit on your stack.