How Does AiBC Help Enterprises
Map Their AI Usage?

01The AI Visibility Gap

Before you can govern AI, you need to map AI usage across the organization: who uses what, with which data. In most organizations, AI use grew from the bottom up. People found tools that made them faster and started using them, often before security knew those tools existed. Netskope's 2026 research found the average organization's GenAI prompt volume grew sixfold in a year, and nearly half of GenAI users still rely on personal accounts.

The result is a set of questions that security, compliance and IT leaders are now expected to answer, and usually can't.

Without those answers, AI policy becomes guesswork. Teams block tools people depend on and miss the ones quietly moving customer data. IBM's 2025 breach research found that even among organizations with AI governance policies, only 34% regularly audit for unsanctioned AI.

Regulators and standards bodies are starting to expect these answers too. The NIST AI Risk Management Framework is built on four functions, and one of them, "Map," is about understanding where and how AI is used and in what context. ISO/IEC 42001 asks for a managed inventory of AI systems, and the EU AI Act puts obligations on organizations that deploy AI, not just those that build it. All of them assume you know what's actually in use.

02What Is an AI Usage Map?

03Why Existing Tools Miss It

Most organizations already have some way of looking at AI use. Each one sees part of the picture.

There's a second problem: AI is no longer a separate destination. It's built into tools people already use, like writing assistants inside document editors, copilots inside email and summarizers inside meeting apps. To a network log, that traffic looks like normal use of an approved app. The only reliable way to separate AI activity from everything else, and to see what data went into it, is to look at the interaction where it happens.

04How AiBC Builds the Map

AiBC, Polygraf's AI behavioral control plane, builds the usage map from the endpoint up. The starting point is a lightweight desktop overlay that runs on Windows and Mac.

Classification runs on the device or inside your own environment, not in a vendor's cloud. That matters more than it sounds. If finding out whether a prompt contains sensitive data requires sending the prompt somewhere else first, the mapping tool itself becomes a new place for data to leak. Running small models locally keeps the check fast and keeps the data where it started.

Because the overlay highlights sensitive data as people type, the same capture that builds the map also starts reducing risk on day one. Users see what's sensitive before they hit send, and most correct it themselves.

05Explore a Sample AI Usage Map

Here is what a usage map looks like for a sample 1,200 person organization over 30 days. Pick a department to see its tools, data and risks, or switch on "unsanctioned only" to see the shadow AI layer.

When you look at a real map for the first time, three patterns tend to stand out. Concentration: one team relying heavily on a single unsanctioned tool usually means it's solving a real problem, and the fix is an approved alternative, not a ban. Low volume, high sensitivity: teams like HR and Legal may use AI less, but almost everything they send is sensitive. Approved doesn't mean safe: sanctioned tools still receive customer data and credentials, so approval alone isn't a control.

06The Five Dimensions of an AI Usage Map

A complete map answers the five questions from the start of this article. Each dimension leads to a different decision.

07From Map to Action

A usage map is only worth building if it changes decisions, which is also what separates a control plane from conventional AI governance. Once AiBC has a few weeks of data, the patterns usually point to a short list of moves, each of which can be set as policy in the same dashboard.

The map is most useful as something you revisit, not a one time audit. A monthly review of three numbers is usually enough to show whether things are improving: the share of AI use that runs through unsanctioned tools, the number of sensitive data events per thousand interactions, and how long it takes to approve a tool once a team clearly needs it. If the first two fall and the third gets shorter, the program is working.

08AI Usage Mapping FAQ