Data Governance
Governs data
Quality, lineage, access, retention. Answers: where is the data, who can use it, is it clean?
Category positioning
Why Semantic Governance?
Enterprises already invest in data governance and AI governance. They still get inconsistent reports, contradictory definitions and unreliable AI outputs. The reason is simple: nothing governs meaning.
AI Governance
Governs AI systems
Model risk, bias, monitoring, EU AI Act. Answers: is this AI safe, accountable, compliant?
Semantic Governance
Governs meaning
Shared definitions, canonical concepts, drift control. Answers: do people, systems and AI mean the same thing?
The missing layer
Without shared meaning, data, AI, reporting and compliance become inconsistent. Two teams using the same word (Coverage, Claim Event, Material Damage) build divergent dashboards, divergent models and divergent regulatory reports — all technically correct, all incompatible.
- 1.Data governance assumes the meaning is already agreed. It cleans values, not concepts.
- 2.AI governance assumes the inputs are well-defined. A model trained on drifting concepts produces confidently wrong outputs.
- 3.Semantic governance closes the loop. Definitions become first-class, versioned, owned, auditable assets — and every downstream system resolves against them.
Product hierarchy
How SynsureTech, WikiSure and WikiScreen relate
SynsureTech
Semantic Governance Company
Parent company. Research, methodology, IP.
WikiSure
Semantic Governance Platform
The platform you are looking at. Workflows, registries, reports.
WikiScreen
Semantic Drift Detection & Learning Engine
The engine inside WikiSure that detects drift and accumulates organizational memory.
Read top-to-bottom: SynsureTech builds it · WikiSure is the product · WikiScreen is the engine that powers it.
In one sentence
Data governance answers "is the data correct?". AI governance answers "is the AI safe?". Semantic governance answers the question both of them quietly assume: "do we all agree what this word means?"