Reading the landscape by function, not logo
The AI governance market is crowded and the labels overlap, so a list of company names tells a buyer little. A more useful map sorts vendors by what they actually do to an AI request: catalogue it, fold it into enterprise risk, or enforce policy on it at runtime. These three functions cut across the marketing categories and predict whether a tool will close your specific gap. Organising the landscape this way also explains why two vendors that both claim AI governance can be almost unrelated in practice.
Category one: registries and assessment platforms
This category catalogues AI systems, stores policies and model cards, and runs risk assessments and questionnaires. Its job is structure and reporting, and it does that well, aligning naturally with the documentation expectations in ISO 42001 and the NIST AI Risk Management Framework. The shared limit across vendors here is that they describe systems rather than touch live traffic. They are the right choice when the gap is organisational, an inventory, owners, and a paper trail, and the wrong choice when the gap is in production behaviour.
Category two: GRC and enterprise risk suites
Broader governance, risk, and compliance vendors add AI as one domain within an existing risk programme. Their appeal is consolidation for organisations that already run risk in one platform. The trade-off is that AI is treated like any other risk-register entry, with the document-and-review rhythm that implies. Functionally they share the registry limitation: they map and report exposure but do not sit in the request path, so enforcement of anything they record happens elsewhere.
Category three: runtime governance gateways
The newest category moves the unit of control from the model to the request. These vendors put a layer in the path of every AI call that intercepts it, redacts personal data, enforces policy, routes to approved models, and records the decision as it runs. This is the only category that governs behaviour rather than describing it, and the only one positioned for agentic AI, where actions occur autonomously at runtime. The evidence frameworks ask for is generated as a by-product of enforcement. Difinity sits in this category, built around the runtime control point.
How to use the map
Place your own risk before you shortlist. If the gap is documentation and programme structure, the registry and GRC categories cover it. If the gap is live traffic carrying personal data to external models, or agents acting without supervision, only the runtime category addresses it. Many mature buyers end up combining a registry for management with a gateway for enforcement, because the categories solve different halves of the problem. The names in each category change quickly; the functions, and which one matches your exposure, are the durable part.
Frequently asked questions
How are AI governance companies different from each other?
By function. Some catalogue and assess AI systems, some fold AI into enterprise risk programmes, and some enforce policy on live requests at runtime. Two vendors both labelled AI governance can do almost unrelated jobs.
Which type of AI governance company should I choose?
Match the category to your gap. Documentation and programme structure point to a registry or GRC suite; live-traffic risk and agentic AI point to a runtime gateway. Many teams use one of each.