How to move from AI discovery to AI enforcement

Most enterprise shadow AI programs have completed step one.

They ran discovery, found more AI in the building than expected, and built a spreadsheet .

Then the program stalled.

This pattern is nearly universal.

Discovery is genuinely useful, and it's also where the easy work ends.

Knowing that 37 agents are running, roughly the enterprise average per Microsoft's February 2026 Cyber Pulse research, doesn't change the fact that more than half operate with no security oversight or logging.

An inventory tells you what happened.

Enforcement decides what happens.

What enforcement means for AI Enforcement is the ability to change the outcome of an AI action while it's occurring, not report on it afterward.

For an agent, that means one of four interventions: block the tool from running, scope down what it can reach, gate a specific action behind approval, or terminate the process mid-execution.

These aren't interchangeable: choosing between them is most of the work.

Blocking is blunt and generates the most complaints.

Scoping is the most durable and hardest to configure.

Gating works until the approval queue becomes a formality people click through.