On-Demand Recording
The AI Blind Spot: How to Find Shadow AI, Enforce Policy, and Measure AI Adoption
The live session has wrapped. Watch the full recording on demand to see how IT and security teams find unsanctioned AI tools, enforce AI policy, and measure adoption with evidence.
- Build an inventory of every AI tool in use, approved or not
- Spot Shadow AI and risky data sharing by user, team, or device
- Turn your AI policy into enforceable rules and audit-ready reports
Your hosts
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Nabil Shahid
Sr. Account Executive
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Mike Noseworthy
Head of Support
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Herman le Roux
Customer Success Manager
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Julia Czlapinski
Product Marketing Manager (Host)
See Shadow AI in your own environment
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49 min
Full recording incl. live Q&A
Live Demo
Shadow AI discovery in real time
Free
AI policy template + checklist for attendees
Ready to close your own AI visibility gap? Book a demo with our team or call 1-888-912-9619.
What we covered
Timestamps match the Zoom recording, so you can skip straight to the part you need.
02:30 Welcome and the AI blind spot Julia frames the problem: employees are already using AI, approved or not, and most organizations can’t see it. |
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04:02 From “should we adopt AI” to “how” Nabil on running a discovery phase before buying licenses, so purchases follow real behavior rather than assumptions. |
07:28 What IT teams actually ask Mike on why buying Copilot or ChatGPT licenses doesn’t mean you know how AI is being used. |
09:10 AI risk by industry Tech’s speed-of-experimentation risk versus healthcare’s PHI exposure and stricter privacy obligations. |
12:06 Live demo: AI Insights dashboard AI platforms in use, adoption by user, app vs website time, authorized/unauthorized tagging, and usage trends in BrowseReporter |
17:09 Choosing an AI policy Herman on the cautious “monitor first” approach vs firm allow/block lists, and why one-tool bans push usage onto personal devices. |
25:43 Live demo: enforcing policy with BrowseControl Blocking specific AI sites, blocking the whole AI category with an allow list, blocking AI apps like ChatGPT.exe, and setting rules group by group. |
28:50 Four places to look for AI blind spots Unapproved tools, shadow AI users, sensitive data exposure, and unused AI investments. |
30:09 Before you buy AI licenses at scale Matching tools to departments and roles to avoid low-adoption, low-ROI rollouts. |
31:48 A 4-phase AI governance model Discover, evaluate, control, measure, and why AI policy should never be a one-time decision. |
34:05 Live Q&A Governance maturity, healthy adoption vs high usage, first steps for shadow AI, and investigations. |
A practical AI governance model in 4 phases
The framework the team walked through at 31:48. Governance starts with visibility, not with blocking.
01
Discover
Find out which AI tools employees already use, by user, department, and device, before you buy or block anything.
02
Evaluate
Separate the tools and workflows creating real value from the ones that need restricting by department.
03
Control
Restrict the tools that don’t meet your security or governance requirements, with rules set group by group.
04
Measure
Track adoption over time to see whether your AI strategy is working, and adjust the policy as tools and risks change.
Three AI policy approaches our clients take
What Herman sees in customer programs, from 17:09 in the recording. Many organizations are still waiting to see who succeeds first.
Monitor first, then decide
Allow access, watch how teams use and benefit from AI, then build a policy from that evidence. Herman sees this produce the strongest policies.
Firm allow and block lists
Define exactly what employees can and can’t use. Simple, but HR, finance, and IT all use AI differently, so a single lane can bottleneck work.
Open access, review later
Let employees use any AI and build policy from the findings. Fast, but data is exposed until visibility is in place.
Herman’s analogy: give children paint and paper with clear instructions, walk away for five minutes, and there will still be paint on the walls. Tools and direction aren’t enough on their own. You need visibility so you can step in early and redirect.
Why AI Adoption is Moving Faster than AI Governance
AI is already embedded in everyday work. Employees use it to write, research, analyze, code, summarize, and automate usually well before IT or security knows a new tool exists.
In a KPMG and University of Melbourne study of 48,000 workers across 47 countries, 57% employees admitted to hiding their AI use and passing AI-generated work off as their own. Nearly half had used AI in ways that break company policy, and just as many had pasted company information into public AI tools.
This becomes both a workforce and clarity problem. People hide what they were never told how to do properly, and by the time a tool shows up in a policy discussion, it’s often been in use for months.
And oftentimes, blocking AI doesn’t solve the immediate problem. Blanket bans push usage onto personal devices and unmanaged accounts, where you have no visibility at all, further pushing adoption and company workflows out of your hands.
What organizations actually need is a way to tell the difference between productive, approved AI use and the rest of it: shadow AI, unsafe data sharing, policy violations, and tools that create more risk than value.
In this webinar, we’ll show you how to move from assumptions to evidence, and from a static AI policy to governance that holds up in practice.
Four places to look for your AI blind spots
The four checks our team starts with when reviewing a client’s AI usage (28:50 in the recording).
Unapproved tools
Which AI platforms are being accessed that nobody has authorized? Decide whether to block them or re-evaluate them.
Shadow AI users
Who is using unsanctioned AI, by employee and department, and does that use fit your acceptable use policy?
Sensitive data exposure
Where is regulated or confidential data entering public AI tools, and where do you need to stop it or retrain people?
Unused investments
Which paid AI tools are underused because of process or policy, and where are licenses being wasted?
Turn Your Workplace Shadow AI From a Blind Spot into a Governed Advantage
1. Proactively Identify
Sanctioned and unsanctioned AI platforms across users, teams, and devices, including the shadow AI nobody submitted a ticket for.
2. Quickly Recognize
Risky AI behavior, attempted file transfers, unusual adoption patterns, and potential exposure of sensitive information.
3. Accurately Measure
Where AI adoption is growing, which departments use it most, and where further investment, training, or intervention is warranted.
4. Effectively Enforce
Your AI policy: allow approved tools, restrict high-risk platforms, create group-specific rules, and make acceptable-use policies technically enforceable, not just documented.
5.Intuitively Build
Audit-ready reporting through scheduled reports, alerts, and usage trails for IT, security, compliance, HR, and executive stakeholders.
6. Confidently Optimize
Your AI strategy by identifying which tools deliver real value, where licenses are wasted, and where teams need training, governance, or approved alternatives.
If you work in healthcare, government, financial services, legal services, manufacturing, or any other regulated or data-sensitive industry where high-stakes decisions need strong data behind them, this recording is worth your time.
Gain the visibility and controls you need to support useful AI adoption, without leaving sensitive data, compliance, and organizational policy to chance.
From the Live Q&A
Your questions, answered
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From the team’s client calls, roughly one in five even mention AI in a policy, and often it’s a document someone signed off on that nobody has revisited. CurrentWare doesn’t write the plan for you. It gives you the visibility (BrowseReporter) and the controls (BrowseControl) to build a realistic policy and enforce it, and your Customer Success Manager can review your data with you.
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High usage alone doesn’t tell you much. Healthy adoption looks like steady, repeat use of approved tools spread across the teams you’d expect, rather than a one-off burst for a single project or one power user. What people do inside the tool matters too: using ChatGPT for recipes is very different from pasting in customer data.
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Get visibility before you block anything. Pull together the data on which AI tools are actually in use; the list is almost always longer than expected. That list tells you where the policy conversation needs to start, and whether any individual case needs a closer look.
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Not without evidence. Blocking blindly can push usage onto personal devices where you have no visibility. Gather the facts first: screenshots, web and app history, and AccessPatrol records of file transfers and connected devices build an audit trail for any investigation, whether the cause is ill intent or an ill-informed employee.
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Both. CurrentWare is a user behavior analytics platform covering AI governance, user activity, and data loss prevention. It shows how your team actually uses AI across websites and applications, then lets you control and act on it. You write the policy; CurrentWare helps you enforce it.
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Existing customers can contact their Customer Success team. Organizations evaluating CurrentWare can contact Sales, and Support handles technical issues. All three are reachable at 1-888-912-9619.
Want a second pair of eyes on your AI usage data? Contact your Customer Success Manager or talk to Sales.
Missed it live? Watch on your own time.
The full 33-minute session plus Q&A, and the AI governance toolkit that came with it.