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AI infrastructure is a new kind of external surface, and it is being deployed faster than it is being inventoried. An MCP server, an LLM gateway, or a model runtime is a network service like any other, except that it often ships with no authentication, reaches internal tools on purpose, and is stood up by a team that does not think of itself as running production infrastructure. AI surface finds that infrastructure on your surface and tells you what it is.

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Surface types

Anything recognised as AI infrastructure but not yet classified is kept as unknown rather than dropped, so the surface stays complete.

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What a row tells you

Each AI surface row identifies both the service and the AI stack behind it:
  • Where it is - hostname, IP, port, protocol, URL, and HTTP title and status.
  • What it runs - the provider, the product, and the model name where these can be determined.
  • How exposed it is - a risk level and an exposure summary in plain words.
  • Why we say so - the underlying evidence, carried with a schema so you can parse it rather than scrape it.

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Triage

Rows open filtered to open status, so you start on what has not been dealt with. From there, filter by risk, provider, product, or the source that found it, and work rows through the same lifecycle as every other finding: accept, resolve, ignore, false positive, or reopen.
Start with risk, then provider. A critical row on a provider your organisation has never bought is usually shadow AI, and worth a conversation before a ticket.

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Hunt AI infrastructure everywhere

AI surface covers the assets in your project. The same classification runs across the whole internet map, so you can hunt AI infrastructure outside your own inventory with InfraQL:
Narrow it to a branch of the taxonomy to ask a sharper question:

Browse the AI taxonomy

See every software/ai path, from agents and RAG to model runtimes and observability.

Back to the external surface

Inventory, findings, DNS risks, typosquatting, and secrets.