2023 was the year 

. A few years into the boom and AI tools are already deeply embedded into how we work. 

. From simple tasks like writing emails to powering agentic systems that execute multi-step tasks autonomously, AI has not-so-subtly become the productivity engine behind the scenes of most organizations.

But there’s no such thing as a free lunch. With its use come growing gaps in security. While personal AI use in the workplace has become nearly universal, 

. Shadow AI—unsanctioned AI tool use—forces a familiar tension I often hear from security leaders, sometimes every week: either block AI and lose productivity, or allow it freely and accept risk. 

Neither extreme is ideal. Unapproved AI use slips in risks we can’t see, but outright blocking it all can take away useful productivity gains from your business. 

So how do we actually solve this paradox, especially at scale?

Enable AI—with the right guardrails in place

There it is. AI is already part of the workday, so the real challenge is giving employees room to use it without opening the door to data exposure (not to mention compliance gaps). Here are four key areas to keep usage in check:

Visibility 

Everything starts here. If you want to manage AI risk, you need a clean inventory of what’s being used across your environment. That means being able to scroll through a live list of AI applications and quickly find:

• Which apps are in use.
• Which users are accessing them.
• Where they’re being accessed from.
• What security and compliance attributes each app has.

This is where many teams get their first surprise—a long trail of unknown or sanctioned apps. Seeing which of these applications are gaining traction can also help better assess risk and prioritize the right gaps. 

Analysis   

• Is the app enterprise-ready?
• Does it meet compliance requirements?
• What is the organization's readiness posture for this tool?
  Context is the difference between awareness and informed risk management. 

Real-time monitoring

Classification 

Control 

Finally, organizations need the ability to enforce policies. Of course, this doesn’t mean blunt-force blocking. Effective teams actually rely on granular controls such as:

• Allowing prompts but preventing file uploads.
• Blocking high-risk applications entirely.
• Restricting personal accounts from being used.
• Preventing sensitive data from leaving the environment. 

Control is what makes safe AI adoption possible and sustainable. Organizations get to apply consistent rules that protect their data, while employees get to use the AI tools that make them more productive. Everybody wins.

AI and data protection don’t have to be at odds