Generative AI (GenAI) is already deeply embedded in enterprises, whether managers realize it or not. Sales teams use it to craft emails, engineers run agents that generate and test code, and marketers rely on it for copywriting and campaign ideation. And much of this is happening without formal approval, oversight, or control.

This is known as Shadow LLM or Shadow AI: the unapproved and unsupervised use of GenAI tools that often flies completely under the radar.

Similar to Shadow IT, visibility is critical for security teams to gain insight into which GenAI tools are in use, how they’re being used, and which users present the greatest risk.

This isn’t driven by bad intent, as end users are turning to GenAI tools with good reasons. It’s simply a case of speed outpacing oversight. These tools are easy to access, quick to deploy, and incredibly productive but they are also difficult to monitor. In most cases, there’s no audit trail to follow when something goes wrong.

The numbers bear this out. A recent Palo Alto Networks “The State of Generative AI” report found that GenAI traffic has surged by 890%. A separate survey of European legal teams revealed that while over 90% of firms are using AI tools, only 18% have implemented any form of formal governance.

It’s in between the gaps of fast-moving technology and slow-moving governance that trouble usually starts. Without clear rules and policies, there is a risk of exposing sensitive data, automating decisions without oversight, and creating blind spots in GenAI usage.

That’s why companies need a GenAI policy to keep them safe in terms of both regulation and compliance and security. If GenAI is already part of how your business runs (and it probably is), you need clear guardrails and policy enforcement around what’s allowed, what’s not, and who’s responsible for enforcement. But a policy on paper isn’t sufficient. Like any effective governance, it has to both adapt to and shape how GenAI gets used day-to-day.

What an Effective GenAI Policy Should Enforce

A GenAI policy does not have to slow things down. It simply makes sure the tools your teams rely on can be trusted, especially when the tools themselves start making decisions, moving data, or acting on behalf of the business. Policies should cover six key areas:

1. Approval of GenAI Chatbots and Third-Party Applications
No GenAI tool should get the green light without a proper review. That means taking a close look at what the tool actually does, how it connects to your systems, who built it, and what it does with your data – whether it’s a chatbot, a plug-in, or a third-party app.

2. GenAI Application Inventory and Ownership Assignment
It’s hard to secure what you don’t track. Every GenAI system in use – internal or external – needs to be logged in a central inventory. And someone needs to be responsible for it with clear ownership that shouldn’t be vague or shared.

3. Access Controls and Permissions Management
GenAI chatbots or tools should follow the same access rules as everything else. That means limiting what tools and agents can see or do, based on their roles, and reviewing those permissions regularly with specific roles to assess who can access what content.

4. Logging and Audit Trails
If something goes wrong, you need to know what happened. That’s why one of the key parts of Shadow AI is to log GenAI interactions, across all data flows for both inputs and outputs, and alert administrators of risky behavior.  

5. Testing and Red Teaming
Assuming GenAI systems will behave as intended should not be assumed. They need to be tested before deployment and on an ongoing basis. That includes red teaming, simulations, and checks for specific GenAI vulnerabilities and threats like prompt injection, data protection or safety regulations. 

6. Enforcement of GenAI Usage Guardrails
Policies aren’t useful unless they’re enforced. Guardrails that dictate which tools an agent can use, what kind of data it can pull, or when it needs human signoff should be baked into the system. 

Of course, listing what a policy should include is the easy part. Making sure those rules are enforced is where most organizations struggle.

Building the Right GenAI Policy from Day One

It’s one thing to write a policy. It’s another to make it matter.

Plenty of organizations have published GenAI guidelines. Fewer have built in the governance muscle to apply them consistently, across teams, tools, and evolving use cases. Too many organizations have GenAI policies within documents no one reads, or that seem clear in theory but fall apart when applied to real workflows, deployment processes, or developer tools. That failure stems from a lack of connection, between what the policy says and how people actually work. Between rules and the systems meant to enforce them, and between ownership and accountability.

Making a GenAI policy work means turning it into something operational. That includes the right controls, the right visibility, and the right people on the hook for keeping it current. Because once GenAI is embedded in your business, the policy isn’t the starting point, it’s the safety net. And if that net isn’t in place when something goes wrong, it’s already too late.