Shadow AI: How to control the use of Generative AI in the company?

What shadow AI is?

Shadow AI is the use of generative AI tools by employees without the knowledge or approval of the organization. It is a form of shadow IT, the use of unsanctioned technology at work, but specific to AI assistants and the many tools now built on them.

It arises for an understandable reason: these tools are easy to access and genuinely useful. Staff turn to them to write faster, summarize documents, draft code or analyze data, usually with no bad intent, simply trying to do their jobs better. The issue is what happens to the information they use. When an employee pastes company data into a prompt, that data can leave the organization, be stored by a third party or even feed the training of an external model.

This makes shadow AI a governance problem rather than a tooling one. The organization has lost visibility of how AI is being used with its data, and cannot protect what it cannot see. Controlling shadow AI, as the rest of this page explains, is about regaining that visibility and control, through a secure web gateway, a cloud access security broker and data loss prevention, without simply banning tools that bring real value.

The risks of shadow AI

Shadow AI has moved quickly up the priority list for security leaders, and for good reason. Its risks are concrete and, because the usage is invisible, hard to manage without deliberate controls.

SWG, CASB and DLP compared

Controlling shadow AI relies on three complementary technologies, each addressing a different part of the problem. The table shows what each does and the role it plays against shadow AI.

Govern, do not ban

The instinctive response to shadow AI, banning generative AI outright, is usually the wrong one. It is worth understanding why, because the better path shapes how the controls are used.

A blanket ban rarely works. AI tools deliver real productivity gains, and forbidding them tends to push usage further underground rather than end it, making shadow AI worse and even less visible. Staff who find a tool genuinely helpful will often find a way around a simple block, and the organization loses both the benefit and the visibility.

The sound approach is to govern AI rather than forbid it. That means gaining visibility of what is being used, offering approved tools so people have a safe option, and applying controls that permit beneficial use while preventing the risky behavior, above all sending sensitive data to an unapproved tool. Because policies can be set per user profile, different roles can be given the access appropriate to their needs, a developer, a marketer and a finance user need not have identical rules. The goal is to enable AI safely, capturing its value while keeping data protected and usage visible.

Putting control in place

Turning the principle of govern, do not ban into practice follows a clear path. These steps move an organization from an invisible shadow AI problem to controlled, visible and safe AI use.

Shadow AI control and Teldat

Controlling shadow AI needs a secure web gateway, cloud application governance and data loss prevention, working together with per profile policy. Teldat brings these together in be.Safe Pro, from a European base.

FAQ’s about shadow AI

❯ What is shadow AI?

Shadow AI is the use of generative AI tools by employees without the knowledge or approval of their organization. It is a form of shadow IT, the use of unsanctioned technology at work, but specific to AI assistants and similar tools. It happens because these tools are easy to reach and genuinely helpful, so people adopt them to do their jobs faster, often without malicious intent. The problem is that when an employee pastes company information into a prompt, that data may leave the organization, be stored by a third party or even be used to train an external model, all outside the company’s visibility and control. Shadow AI is therefore not a tooling problem but a governance one: the organization has lost sight of how AI is being used with its data.

❯ Why is shadow AI a security risk?

Shadow AI is a security risk mainly because of data leakage through prompts. When staff paste sensitive information, source code, customer data, financial figures or strategy, into an external AI tool to get help, that data leaves the organization’s control and may be retained or processed in ways the company never agreed to. Beyond leakage, shadow AI creates a loss of visibility: security teams cannot protect or govern what they cannot see, so they do not know which tools are in use, by whom or with what data. There are also compliance implications, since sending regulated data to an unapproved service can breach data protection obligations. The combination of invisible usage and uncontrolled data flow is what makes shadow AI a priority for security leaders.

❯ How do SWG, CASB and DLP help control AI use?

These three controls address different parts of the problem and work best together. A secure web gateway, or SWG, sits between users and the internet and can see and control access to AI tools, allowing, blocking or restricting them by category or by specific application. A cloud access security broker, or CASB, governs the use of cloud services and AI applications, giving visibility of which are used and applying policy to them. Data loss prevention, or DLP, inspects the content being sent and can stop sensitive data from leaving, for example blocking a prompt that contains confidential information. Used together, they let an organization see which AI tools are used, control access by category and application, and prevent sensitive data from being sent, all governed by policies set per user profile.

❯ Should companies ban generative AI to stop shadow AI?

Banning generative AI outright is usually neither practical nor wise. AI tools deliver real productivity benefits, and a blanket ban tends to push usage further underground rather than eliminate it, making shadow AI worse and less visible. A better approach is to govern AI rather than forbid it: gain visibility of what is being used, provide approved tools so staff have a safe option, and apply controls that allow beneficial use while preventing the risky behavior, such as sending sensitive data to an unapproved tool. Policies can be tailored per user profile, so different roles get access appropriate to their needs. The goal is to enable AI safely, capturing its benefits while keeping data protected and usage visible, not to say no to AI.

❯ How does Teldat help control shadow AI?

Teldat addresses shadow AI through be.Safe Pro, its cloud delivered network security service, which brings together the controls needed to govern AI use. Its secure web gateway sees and controls access to AI tools, allowing, blocking or restricting them by category or by specific application, so an organization gains visibility and control over which tools are used. Its cloud access security broker capabilities govern cloud and AI application use, and its data loss prevention inspects traffic to stop sensitive data from being sent in prompts. Because policies can be set per user profile, different teams can be given access suited to their role, enabling AI safely rather than banning it. As a European manufacturer under European jurisdiction, Teldat also supports the data sovereignty and compliance goals that matter when governing where corporate data goes. Specific capabilities should always be verified for each environment.