AI adoption is accelerating across every enterprise, often faster than security teams can track. Employees are using copilots, browser plug-ins, agents, and AI-enabled SaaS tools to write code, automate workflows, analyze data, and increase productivity across the organization.
This rapid adoption is often framed as a competitive advantage. But beneath the productivity gains lies a growing security problem that many organizations are only beginning to recognize. AI is entering corporate environments without approvals, without documentation, and without security review. In many cases, it is already embedded in daily workflows before security teams are even aware of its presence.
This is shadow AI. And while it shares similarities with shadow IT, the risks associated with shadow AI emerge faster, spread wider, and are far more difficult to contain.
Why Security Leaders Are Alarmed by AI Adoption
Security leaders are not concerned about AI simply because it is new. Their concern stems from the way AI changes how work gets done and how data, access, and identities move across the organization.
Access risk compounds the problem. Most AI tools do not operate in isolation. They rely on OAuth grants, API tokens, service accounts, or other integrations to function. Through these mechanisms, AI tools can gain direct access to systems such as Google Workspace, Salesforce, Jira, GitHub, Slack, and cloud environments. When those permissions are overly broad or poorly understood, the blast radius expands rapidly.
Compliance and audit pressure only intensifies the urgency. Security and compliance teams are increasingly asked to explain which AI tools are in use, who approved them, what data they access, and what controls are in place. For many organizations, these questions are difficult to answer with confidence because they lack a reliable way to see AI usage across the environment.
Adding to the challenge is the pace of adoption. AI tools can be introduced and widely adopted in days, while security reviews and approval workflows often take weeks. This mismatch allows shadow AI to take root long before governance can catch up.
Why Shadow AI Is More Dangerous Than Shadow IT
Shadow IT traditionally introduces unmanaged applications. Shadow AI introduces unmanaged identities.
AI tools and agents commonly create OAuth grants, API tokens, service accounts, bot users, and agent identities as part of their operation. These identities often persist long after a pilot or experiment ends. They are rarely revisited, frequently forgotten, and often retain access to sensitive systems.
In large enterprises, this can quickly result in hundreds or thousands of AI-linked identities operating quietly in the background. Without visibility into their existence and permissions, security teams have no practical way to assess risk or enforce least-privilege access.
The Visibility Gap That Makes Shadow AI Dangerous
Many AI security discussions focus on governance. Organizations debate which tools should be allowed, which should be restricted, and what policies should guide AI usage. While governance is essential, it cannot function without visibility.
If security teams do not know which AI tools are being used, which identities they have created, what systems they can access, and who owns them, policy enforcement becomes ineffective. Risk management turns into guesswork rather than informed decision-making.
This is where AI discovery becomes critical.
How Wing AI Discovery Brings Shadow AI Into the Light
Wing AI Discovery is designed to give security teams immediate visibility into AI usage across the enterprise. Rather than relying on self-reporting or manual audits, Wing continuously discovers AI tools, agents, and AI-enabled integrations already operating in the environment.
Wing identifies AI tools in use, the OAuth grants, API tokens, service accounts, and agent identities they create, and the systems they connect to. It provides a clear view into what access each AI tool has, where permissions may be excessive, and which connections introduce the highest risk.
Just as importantly, Wing helps teams understand ownership. By mapping AI tools and integrations back to users and teams, security leaders can move beyond detection and toward accountability and remediation.
With this level of visibility, security teams can finally answer the questions they are being asked every day:
- Which AI tools are active right now?
- Where are they connected?
- What access do they have?
- Which permissions are risky or unnecessary?
- Who is responsible for each tool or integration?
From Discovery to Control
AI discovery is not an end goal. It is the foundation for control. Once AI usage is visible, security teams can take meaningful action. They can tighten permissions, remove unused or risky connections, enforce approval workflows, and establish safer pathways for teams to adopt AI without introducing unmanaged risk.
Most importantly, discovery allows security to keep pace with the business. Instead of reacting after AI has already spread, teams can proactively monitor adoption as it happens and address risk before it becomes unmanageable.
Securing AI Without Slowing the Business
The goal of AI security is not to block innovation or slow adoption. AI is already embedded in how modern organizations operate, and attempts to stop its use are unlikely to succeed. The real goal is to eliminate blind spots.
Shadow AI becomes dangerous when it operates invisibly, creating access and moving data without oversight. Wing AI Discovery helps security teams regain visibility, reduce risk, and support responsible AI adoption at scale.
AI is no longer a future concern. It is already present, already connected, and already creating access across the enterprise. For organizations that want to govern AI effectively, discovery is not just the first step. It is the most urgent one.
To learn more – schedule a demo today.