Govern the context AI uses—not only the data it stores.
Kynexa extends enterprise data governance into retrieval, context assembly, inference, agent memory and generated outputs with semantic metadata, purpose-aware policy, lineage and audit.
- Catalogs describe files; AI works on chunks & entities
- Memory and recall ungoverned
- No purpose-aware policy
AI changes the unit of governance.
Catalogs and access controls describe files, tables, columns and owners. AI systems work across chunks, entities, relationships, prompts, memory and generated context. Data governance teams need to know not only what data exists, but how it is selected, combined, transformed and used in an AI interaction.
What data governance leaders get
AI-reachable data inventory
Identify sources, assets, chunks, classifications, owners and the AI systems that consume them.
Semantic metadata and context graph
Represent entities, relationships, sensitivity, taxonomy, purpose, provenance and lineage across sources.
Purpose-aware retrieval policy
Allow, deny, redact, or scope context based on identity, role, purpose, sensitivity and policy obligations.
Memory governance
Apply retention, recall, sharing and deletion controls to agent memory as governed enterprise context.
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 unstructured and structured data catalog and governance platform on your stack.