Beyond Shadow AI: Is “Shady AI” the Next AI Governance Risk?

Early AI governance efforts have largely focused on identifying tools employees use without organizational approval. That concern, commonly referred to as shadow AI, remains important. But the risk does not end with unapproved tools. As AI becomes embedded in everyday business applications, organizations also need to consider a different question: What happens when the tool is approved, but the use case is not?

An emerging term for this risk is “shady AI,” which describes the use of approved AI tools in unexpected, unapproved, or poorly governed ways. Unlike shadow AI, where employees use AI tools outside an organization’s approved technology environment, shady AI can happen within that environment using tools the organization has already authorized.

The term is relatively new and is not a formally established cybersecurity category under frameworks from organizations such as NIST, OWASP, or MITRE. The underlying governance challenge, however, is not new and should not be overlooked.

Approval Does Not Equal Control

Traditional technology governance has often centered on the approval process. An application is evaluated, security requirements are considered, access is established, and the technology is put into use. AI introduces a new challenge to that governance model.

AI capabilities are increasingly being added to platforms organizations already rely on, expanding what employees can do within those systems. AI assistants and agents, for example, may be able to access enterprise information, interact with applications, or automate workflows. As those capabilities grow, so can the potential uses of an approved platform, including uses that were never part of the original evaluation.

For example, an AI application initially approved to summarize information might later be used to analyze confidential documents, access internal data, automate workflows, or support business decisions. Those uses may introduce risks that were not considered when the application was originally approved. The application may still be approved, but the use case may not be.

That distinction should change how organizations approach AI risk. It is no longer enough to know which AI technologies are being used. Organizations also need visibility into how they are being used, what information they can access, and what actions they can take.

When Approved AI Creates New Risks

Shadow AI creates a visibility challenge because organizations cannot govern technology they do not know employees are using.

Shady AI creates a different challenge. The technology is known and approved, which can create the assumption that the associated risks have already been addressed. But new features, broader permissions, and additional integrations can expand both what an approved AI tool can do and how employees use it.

A technology originally approved for a limited purpose may gradually take on a larger role without a corresponding risk review. This is particularly important as AI becomes more deeply embedded in existing enterprise software, where platforms may add or expand AI functionality over time without necessarily receiving the same scrutiny as a new application.

AI governance cannot end with approval. It requires ongoing visibility into how AI capabilities and their use evolve.

Rethinking What “Approved” Means

Organizations should think more precisely about what it means for an AI tool to be “approved.” Approval is an important starting point, but it does not account for every way a tool may eventually be used.

As capabilities and use cases change, organizations may need to reassess whether an approved tool is still operating within the boundaries originally considered. That means looking beyond the technology itself and considering the information it can access, the systems it can interact with, and the actions it can take.

Ultimately, AI governance needs to evolve along with the tools themselves and the ways employees use them.

Managing Risk Without Slowing Adoption

Keeping pace with AI use does not necessarily mean adding more restrictions. In fact, overly restrictive policies can create a different problem. If approved tools do not meet legitimate business needs, employees may look for other solutions, potentially pushing AI use back into the shadows.

The goal is to put enough structure around AI use to manage risk without creating unnecessary friction. That starts with understanding which AI-enabled applications are in use, how employees are using them, what information they can access, and when changes in capabilities, permissions, or use cases should trigger a new risk review.

The goal is to create enough structure to manage risk without getting in the way of how people work. That starts with understanding which AI-enabled applications are in use, how they are being used, what information they can access, and when changes in capabilities, permissions, or use cases warrant another look.

Employees also need clear, practical guidance. AI has evolved quickly, and policies developed when generative AI first entered the workplace may no longer reflect what today’s AI tools and agents can do. Employees need to understand not only which tools they can use, but how to use them responsibly.

Preparing for the Next Phase of AI Risk

As AI becomes part of everyday business, one-time approval is no longer enough. Organizations need to keep pace with how these tools are actually being used and how that use is evolving.

Citrin Cooperman’s Risk Advisory professionals can help you assess your current AI environment, identify potential governance gaps, and develop a practical approach to managing AI risk while supporting responsible adoption.