Finding the AI actually running across your business rarely comes down to one method. It comes from combining several: watching what happens in the browser, scanning what’s installed on a device, reviewing identity logs, tracing network traffic, and checking code repositories for embedded models. Each one catches something the others miss, which is exactly why so many teams try to stand all of them up at once.
That’s usually the wrong move. Some of these signals are ready to use with infrastructure you already have. Others take real integration work before they produce anything useful. Treating all of them as equally urgent is how a discovery effort stalls before it delivers a single useful finding. It is the same mistake as trying to renovate an entire house at once instead of finishing one room and living in it while the rest gets planned properly. The house eventually gets finished either way, but only one approach gives you a livable space in the meantime.
Start with what you can already see
Two signal types tend to be the fastest to put in place, because they build on tools most organizations already run. Endpoint based scanning looks at installed applications and local environments to catch AI capabilities operating outside approved channels, using the same kind of agent many security teams already deploy for other purposes. Browser based monitoring works similarly: it watches AI activity in real time as people use it, which means it can start surfacing findings almost as soon as it’s turned on, without waiting on a separate infrastructure project.
Neither of these requires you to touch your network architecture or renegotiate anything with a vendor. That’s precisely why they tend to produce results faster than the alternatives. A team can typically turn both on, review the first results, and start making decisions within the same week, which is a meaningfully different experience than waiting on a cross functional integration project to finish before anyone sees a single finding.
Identity signals fill a different gap
Endpoint and browser monitoring both see activity, but neither one tells you who is responsible for it. Reviewing identity provider logs closes that gap by connecting AI usage to specific users and roles rather than just a device or a browser session. That context matters once you move from finding shadow AI to actually governing it, because “someone in finance is using an unapproved tool” is a very different problem to solve than “the head of financial reporting is.”
This kind of visibility tends to be lower effort than it sounds, because most organizations already route authentication through a central identity provider. Reading what’s already flowing through that system is a smaller lift than deploying something new. It also tends to surface a different category of finding than endpoint or browser monitoring does: patterns of access rather than individual instances of use, which matters when you are trying to understand not just what AI exists but how deeply it has been adopted across a specific team or function.
Network and code visibility earn their place, just not first
Two other signal types matter just as much in the long run, but they typically require more setup before they pay off. Tracing AI traffic across your network infrastructure means integrating with the controls that already govern that traffic, which is a heavier lift than turning on an endpoint agent or a browser monitor. Scanning code repositories for embedded models, API calls, and AI agents is valuable, especially for engineering heavy organizations, but it depends on integrating across however many repositories your teams maintain, and that number only grows over time.
Neither of these should be skipped permanently. They simply aren’t where a team should start if the goal is to get useful visibility in front of leadership quickly. A rollout that begins with endpoint, browser, and identity signals can produce a real, usable picture while the deeper network and code level work gets scoped properly instead of rushed. Rushing either one to hit an arbitrary deadline tends to produce worse outcomes than taking the time to integrate them properly once the easier signals have already bought you some breathing room and an initial picture to work from.
Why the order matters more than the checklist
Two teams can technically deploy the same five signal types and end up in very different places. One tries to launch all of them simultaneously, spends months on integration work before anything is usable, and struggles to show progress. The other starts with the two or three signals that need the least setup, gets a working picture of its AI footprint in weeks, and uses that early result to justify investing in the deeper signals next.
The second approach isn’t just faster. It’s also easier to defend internally, because a partial but working inventory is something you can show a board or an auditor today. An ambitious plan that hasn’t produced a single result yet is much harder to explain.
Discovery is a moving target either way
None of this is a one time project regardless of where you start. New AI tools, models, and integrations show up continuously, whether through an employee trying something new, a developer wiring a model into an internal tool, or a SaaS vendor quietly turning on a new AI feature by default. A centralized AI inventory that pulls identity, network, application, and code signals into one place is what keeps that picture current instead of letting it go stale the way a one time audit inevitably does.
AI Discovery is built around bringing exactly those signal types together, so the visibility you build by starting narrow keeps expanding without turning into a second full project later.
Getting a working picture of your AI footprint doesn’t require solving every signal type on day one. It requires picking the right ones to start with, and building from there.
Govern what you find by starting with visibility you can put in place this week. Connect with the Airia team to scope where your organization should begin.
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