The data governance market is splitting in two. On one side: catalog-first platforms that document assets passively. On the other: active governance systems that enforce policies, manage access, track lineage, and produce evidence as work happens.
Active vs. passive governance
Passive governance documents what exists. Active governance enforces what should happen. The difference is operational: access is governed at runtime, quality is measured continuously, lineage powers impact analysis, and evidence is a byproduct of work — not a compliance exercise.
What leaders are doing differently
- Building a unified metadata graph across data, analytics, and AI assets.
- Connecting lineage, quality, and policy into a single operational layer.
- Governing AI systems with the same rigor as enterprise data.
- Producing audit-ready evidence automatically, not manually.
The AI governance imperative
AI governance is no longer optional. Models, prompts, agents, RAG pipelines, and vector stores must be governed with the same metadata graph, policy engine, and evidence layer as enterprise data. The enterprises that govern AI separately from data are building technical debt they will pay for at audit time.