It used to be that only professional athletes and movie stars had agents working for them, but in the AI era of today, anyone can have an agent working for them. The mass availability of these agents creates a serious question for your organization:

Can your employees independently create AI agents?

AI Sprawl is Present and Accelerating

If you don’t have the answer to that question, there is a good chance they are. The rapid adoption of AI inside companies closely mirrors the early days of cloud computing, when Shadow IT first became both a buzzword and a real concern. AI adoption is happening even faster, however, because individual employees can access powerful tools almost immediately through free trials or inexpensive personal subscriptions.

The result is growing AI sprawl throughout the enterprise that operates outside of IT controls. This sprawl lacks in visibility, ownership and established policies. AI agents introduce additional security considerations because they can interact with internal services, external APIs, sensitive information, and business workflows. Left unchecked, this hidden layer of AI activity can introduce security risks, compliance gaps, and inconsistent outcomes that the organization never intended.

CIS Introduces New AI Controls

Whether it’s Shadow IT or Shadow AI, the story is the same: You can can’t secure what you can’t see. If you don’t know what you have, you cannot implement a security program around to control it. This goes to the very heart of the Center for Internet Security (CIS) Controls:

• Control 1: Inventory and Control of Enterprise Assets.
• Control 2: Inventory and Control of Software Assets.

In fact, CIS has now updated their controls in v8.1 to include what they should be inventorying for around AI. The first step organizations need to take is discovering and inventorying AI applications, models, and other assets. It’s only at that point that you can begin thinking about controls.

AI discovery is not a one-time assessment, however. It is an ongoing process, and for good reasons. The AI ecosphere inside any organization is constantly shifting:

• New AI tools enter the environment as employees seek faster, more efficient ways to work
• SaaS vendors quietly enable AI features inside applications that were already approved
• Agentic workflows grow more complex as AI agents spawn additional sub-agents
• AI use extends beyond managed devices to local models and off-network tools that traditional discovery mechanisms may never see

Without a deliberate and continuous discovery process, users will adopt AI tools faster than IT and security teams can identify, evaluate, and control them.

Creating a Corporate AI Usage Policy

Your biggest concern is that AI can access your data, and you must accept this truth. That’s why when you create an agent, it must have certain properties that make it controllable, trackable, and temporary in many cases. Your business must have clear policies that define:

• Who is allowed to use AI
• Which systems they can use
• What data those systems may access
• Where that data is permitted to flow

Every agent should have an assigned owner, a defined purpose, an explicit end date, and operate under strict least‑privilege permissions for its tasks.

The Purpose of Centralized AI Gateways

Mapping the relationships between users, AI agents, and the systems they interact with can quickly resemble a detective’s evidence board, with strings crisscrossing in every direction to connect people, applications, data sources, and automated processes. As more agents enter the environment, those connections become increasingly difficult to trace, making it harder for IT and security teams to understand who or what has access to sensitive information.

Once AI applications and assets have been identified, organizations can route access through a managed gateway that provides centralized visibility and control. Rather than allowing AI connections to proliferate across the environment in an ungoverned way, an AI gateway establishes a single, consistent point through which access is managed and usage policies are enforced. This approach brings several important benefits:

• Enforces approved AI usage policies consistently across the organization
• Centralizes access management for both sanctioned and shadow AI tools
• Strengthens security oversight and simplifies operational management
• Replaces fragmented, ungoverned AI connections with a controlled and auditable architecture

With a centralized AI gateway in place, organizations can start enforcing operational controls such as traffic routing, usage quotas, and cost management. This is in tandem with security controls for identity, authorization, inspection, and auditing without having to configure and manage them separately for every AI connection.

Conclusion

The days of your employees Googling for answers are over. Today, they’re using third-party GenAI outlets and other AI agentic tools you aren’t even aware of. AI sprawl is an unfortunate reality for many organizations today, but it doesn’t have to be. Our AI security engineers at WEI can show you how to gain control over your environment. Our 3-step plan of discovery, routing and control can help ensure that your security is staying ahead of your organization’s insatiable appetite for more AI.

Next Steps: This free WEI solution brief outlines the strategic capabilities, program gaps, and organizational capacities that define AI-era security maturity. Synthesizing leading frameworks with real-world insights from the WEI CISO community to answer the question every security leader is asking: What are other organizations in my space doing today?

DOWNLOAD SOLUTION BRIEF: Defending at Machine Speed