What an AI governance solution is for
An AI governance solution exists to keep an organisation in control of how its AI systems behave: which data they touch, which policies apply, and whether you can prove any of it to an auditor. The category has grown quickly because two pressures arrived at once. Regulation, led by the EU AI Act and ISO 42001, raised the bar on what control you must demonstrate. And real incidents, from personal data leaving for external models to assistants inventing policy, showed that good intentions do not survive contact with production. A governance solution is how a team turns intent into something it can enforce and evidence.
The main categories on the market
Most solutions fall into one of three shapes. Registries and assessment tools catalogue your AI systems, store policies and model cards, and score risk through questionnaires. GRC and risk suites fold AI into a wider enterprise risk programme. Runtime governance layers sit in the path of every AI request and enforce policy as the request runs. The categories are not interchangeable: the first two are built to document and report, while the third is built to intercept and stop. Knowing which problem you actually have keeps you from buying a reporting tool when your risk is in live traffic.
What to match against your risk
Start from where your exposure sits. If your gap is organisational, you lack an inventory, owners, and a paper trail, a registry closes it. If you already run enterprise risk in one place and want AI folded in, a GRC suite consolidates it. If your exposure is in production, where requests carry personal data to external providers or agents act without supervision, only a runtime layer governs the behaviour rather than describing it. Many mature programmes end up running a registry for management and a runtime layer for enforcement, because the two solve different halves of the same problem.
What a runtime solution actually does
A runtime governance layer routes every AI request through one endpoint. It intercepts the request, redacts personal data before it leaves the boundary, enforces the policy that applies to that user and use case, sends the request only to approved models, and writes an audit record as it goes. Because enforcement and evidence happen in the same pass, the audit trail an EU AI Act or ISO 42001 review demands is generated automatically rather than reconstructed later. For agentic systems, this is the point where a tool call can be checked and blocked before it executes.
How to evaluate a shortlist
Test claims against behaviour, not feature sheets. Send a request that should be blocked and watch whether the solution stops it before the model runs or only logs that it happened. Paste data that should be redacted and confirm it never reaches the provider. Ask to see the audit record for that exact request and check it maps to a named obligation. Confirm policy can differ by team on one layer. A solution that passes these on live traffic is governing AI; one that only produces a nice report is documenting it.
Frequently asked questions
What is the difference between AI governance software and a runtime layer?
Most governance software documents and reports on AI systems. A runtime layer sits in the request path and enforces policy as each request runs, producing the audit evidence as a by-product of enforcement.
Do I need more than one AI governance solution?
Often, yes. Many teams pair a registry for programme management with a runtime layer for enforcement, because documentation and live control address different parts of the same obligation.