How big a problem is shadow AI in large enterprises today?

Large, and unmeasured, which is the more useful half of the answer. Most security teams cannot name the AI tools running inside their own workforce, so the honest response to the size question is that nobody has one yet.

Shadow AI is the cloud-era shadow IT problem with one change in the mechanics. Shadow SaaS eventually touched procurement, because someone needed a card, a contract, or an admin to provision seats. A browser extension that summarizes documents needs a click. No purchase, no ticket, no footprint in any system you control.

The exposure is different too. Shadow SaaS stored your data. Shadow AI reads it. Employees paste contracts, customer records, incident notes, and source code into models whose retention terms nobody on your team has reviewed, and the material is gone the moment the request is sent. There is no recall.

Scale surprises teams when they finally measure it, because adoption spreads sideways rather than top-down. One person finds a tool that saves an hour a day and tells their team. Within a quarter it is departmental, still invisible, and increasingly load-bearing for work that has to get done.

Akto Atlas was built for that measurement gap. It discovers AI tool and agent usage across the enterprise, maps what each one can reach, and applies guardrails, which lets teams govern usage instead of issuing a ban that gets routed around by lunchtime.

Start by counting. Every argument about whether to allow, restrict, or replace these tools is unwinnable until you know what is already running.