The GenAI adoption curve is steep
Generative AI has moved from experiment to everyday tool faster than almost any technology in enterprise history. Employees are using ChatGPT, Claude, Gemini, and dozens of specialized AI tools to write emails, summarize documents, generate code, analyze data, and automate repetitive tasks.
For CIOs, this creates an uncomfortable tension. On one side, GenAI is delivering real productivity gains - the kind that boards and executives are demanding. On the other side, every AI interaction is a potential data leak. Employees are pasting customer data into prompts, uploading proprietary documents for analysis, and sharing confidential information with AI services that may train on that data.
Blocking AI is not an option. Ignoring the risk is not either. The CIOs who are handling this well have found a middle path.
Why traditional controls do not work for AI
Most enterprise security stacks were designed to control access to applications and monitor network traffic. They work by inspecting what goes in and out of the corporate network. But GenAI tools break this model in several ways:
AI interactions happen inside the browser tab. When an employee pastes text into ChatGPT, that interaction never touches the network in a way that a traditional proxy can inspect meaningfully. The data moves from one browser tab to another - from your CRM to an AI prompt - entirely client-side.
The risk is in the content, not the connection. Blocking access to ChatGPT is easy. But employees will just switch to a different AI tool, use a personal device, or find another workaround. The real risk is not which AI tool they use - it is what data they put into it.
Shadow AI is already everywhere. By the time most IT teams start thinking about AI governance, employees have been using AI tools for months. The usage is distributed, varied, and often invisible to traditional monitoring.
The browser as AI control point
CIOs who are managing GenAI risks effectively have converged on a common approach: make the browser the control point.
This makes sense when you think about it. The browser is where AI interactions happen. It is where employees copy data from SaaS apps and paste it into AI prompts. It is where files get uploaded, responses get downloaded, and sensitive information gets shared.
By managing the browser, IT can:
- See which AI tools employees are actually using. Not just which URLs they visit, but how they interact with AI applications - what they paste, upload, and share.
- Set policies per AI tool. Allow ChatGPT for general use but block sensitive data from being pasted. Allow Copilot for code but prevent it from accessing production data. The policies can be granular and context-aware.
- Enforce rules without blocking. Instead of binary allow/block decisions, browser-level controls can warn users when they are about to share sensitive data, redact specific content types automatically, or require approval for certain actions.
- Cover managed and unmanaged devices. Since the control is in the browser itself - not an endpoint agent - it works on any device where the employee uses the work browser.
What this looks like in practice
Here is how leading organizations are implementing browser-based AI governance:
Step 1: Visibility. Deploy an enterprise browser and let employees use it for all work applications. Within days, IT has a complete picture of which AI tools are being used, how often, and what types of data are being shared.
Step 2: Classification. Based on that visibility, categorize AI tools into tiers. Some might be fully approved. Others might be approved with restrictions. Some might be blocked entirely. The key is making these decisions based on actual usage data, not assumptions.
Step 3: Policy enforcement. Apply browser-level policies that match those tiers. For approved tools, allow normal usage but log interactions. For restricted tools, enforce copy/paste controls, upload restrictions, and content filtering. For blocked tools, prevent access entirely.
Step 4: User education. Use the browser to surface contextual guidance. When an employee tries to paste sensitive data into an AI tool, show a brief explanation of why it is being blocked and what alternatives are available. This turns security enforcement into a learning moment.
Step 5: Continuous adjustment. AI tools and usage patterns change fast. Review policies monthly, update tool classifications, and refine controls based on what the data shows.
The balance between control and productivity
The CIOs who get this right share a common mindset: security should enable AI adoption, not prevent it. If your AI governance strategy results in employees working around the rules, it has failed.
The most effective approach is to make the secure path the easy path. Give employees a browser that works great, supports the AI tools they need, and handles the security controls transparently. Most employees do not want to leak data - they just want to get their work done. If the secure way to use AI is also the convenient way, adoption follows naturally.
How dME approaches AI governance
dME is built with AI governance as a core capability, not an afterthought. The browser gives IT teams granular control over how employees interact with AI tools:
- Set per-tool policies for copy/paste, file uploads, and prompt content
- Get visibility into AI usage across the organization
- Apply rules based on user identity, device posture, and data sensitivity
- Deploy to any device without agents or infrastructure
The GenAI train has left the station. The question is not whether your employees will use AI - it is whether you will have visibility and control when they do.