not the hackers. your own people — every day, through AI. 6 questions, 5 minutes, and a straight read on where your confidential and regulated data is actually exposed. no pitch deck. no follow-up call.
no credit card. no opt-in wall. not legal advice — a straight operator's read on your exposure.
for thirty years, security meant keeping people out. locks, passwords, firewalls — everything built to stop the wrong people getting in. AI flipped it. now the risk walks out the front door — willingly, carried by your own team.
this isn't a hunch. 52% of knowledge workers admit to using AI tools their company never approved — while 90% of executives say they're confident they can see what's in use (okta, march 2026). and the average employee puts sensitive data into an AI tool once every three days (cyberhaven, 2026).
it's not malice. it's momentum. they're just trying to get good work done at the speed of AI.
maybe. for the one tool operating on that platform and its data, under that corporate account and plan.
AI governance was never only about the vendor's training policy. it's that you can't name which tools your people use, whose accounts they're on, or what left the building in the last three days. your enterprise contract doesn't cover the personal ChatGPT tab, or the Claude Code session running in a terminal in the next window. nothing does — until you do.
and what's actually going into the unapproved tools? among the workers using them:
why do they do it? 80% say their own account is easier. 57% say the approval process is too slow. that's not a discipline problem — it's a governance design problem. none of these numbers require a hacker. they just require a tuesday.
banning it doesn't work — samsung tried in 2023, and lifted it. your best people would route around a ban by friday, and it would cost you more than the risk. the fix is a governance layer over every team touching AI — lighter than the security stack you already run. three moves.
put another way: the AI your team already uses is a pile of loops running without a control layer — in personal tabs, on personal logins, where you can't see them. governance is that control layer: what each tool can touch, where it stops, and what gets logged. the same layer we build into every loop →
every tool, every account, every personal login. almost no one has done this, and the real inventory is always longer than leadership guesses. it ends in a written Exposure Brief — what's in use, what data it touches, and where you're exposed — a document you can act on, not a conversation you forget.
which classes of data can touch which tools — built on the NIST AI Risk Management Framework with your actual obligations overlaid: the contracts, NDAs, and regulations you're genuinely bound by. a governance program and AI-use policy that fit your business, not a template you downloaded.
one accountable owner, and enforcement at the gateway level — on both surfaces: the shadow AI your team uses and the AI inside your own apps. plus ongoing monitoring, so the rules are still being enforced in month six, not just in the kickoff deck.
one more thing — this lives at the top. AI is in every team, every group, every functional area. not an operations problem. not a finance problem. not (just) an IT problem. the governance layer sits above all of them — and in an owner-led business, that means it sits with you. it's also the first thing a lawyer, an insurer, or a big customer's security questionnaire will ask about.
in Texas? there's a law now. TRAIGA — the Texas Responsible AI Governance Act — took effect January 1, 2026, and it rewards exactly this: documented controls and real records. we build governed-AI systems around that safe harbor. read how a governed compliance system works →
if your exposure is low, you'll know. if it isn't, you'll know exactly where it's leaking — and the first move to make this week.
run the free exposure check →no opt-in required. no sales call. not legal advice.