How to turn Shadow AI findings into working controls

The cover image is an AI-generated editorial illustration. Screens and documents are illustrative concepts.

An AI policy is easier to follow when employees have an approved way to do their work. Start by finding out what they are trying to accomplish, which tools they use and what information those tools can access. Then decide what you will permit and how you will check it.

This is the approach we recommend when using CrowdStrike to address Shadow AI. The work combines technical discovery with decisions about data, access and responsibility.

Establish what you can see

Use a representative group of devices and users to test the discovery coverage. Compare the findings with software inventories, relevant service administration records and conversations with staff. Each source can reveal something the others miss.

CrowdStrike’s AI security announcement describes controls for desktop AI applications and discovery of agents and related components. Verify the support and prerequisites for your particular applications. Do not assume that detecting an installed tool means you can inspect every interaction it makes.

Before collecting prompt content, agree what information is needed, who can read it and how long it will be retained. The monitoring system itself can become a store of sensitive material.

Make approval useful

For each tool, record its purpose, owner, account type and permitted data. Approval should describe a use case, not just a brand. A company-managed account used for public marketing copy is a different decision from a personal account connected to customer records.

Give employees a straightforward route to request a tool or an exception. Some uses should be prohibited; others can be allowed with narrower permissions or a different subscription. Explain the reason so people know what an acceptable alternative looks like.

Test the rule against the task

Translate the decision into a control that the deployed product supports. That may be a warning, a data-handling restriction, an application control or an escalation. Use harmless test data.

For example, if a rule is meant to prevent confidential documents reaching an unapproved service, check both that transfer and an ordinary permitted task. A rule that blocks everything can make the demonstration look successful while making the product unusable.

Look at the alert too. Does it give the responder enough information to act without exposing more employee or customer data than necessary?

Agree who responds

The person writing the policy, the person administering Falcon and the team investigating alerts need the same understanding of the rule. Establish who can approve exceptions, revoke access and contact the affected user.

Do not assume that an existing MDR agreement includes every AI-related alert or new module. Confirm its scope. Falcon Guardian extends CrowdStrike’s AI security offering, and the capabilities and service coverage available in your setup need to be checked.

Review new tools, repeated exceptions and incidents as the business changes. The useful outcome is a set of controls people can work with, backed by someone who keeps them current. FM CyberSecurity’s AI security service can help with that assessment and implementation.