What is shadow AI and how to govern it

Shadow AI is the use of AI tools inside an organization without IT's knowledge or approval. This guide explains what it is, why it creates real security and compliance risk, and how the Bifrost AI gateway together with Bifrost Edge brings that usage under governance on every machine.

Most of the AI tools employees rely on at work run on their own machines and reach a model provider directly, without passing through any corporate network checkpoint. A developer can install a desktop assistant, paste in proprietary source code, and send it to a third-party model before anyone in security knows the tool exists. Industry analysts call this pattern shadow AI: the use of AI tools or applications by employees without the approval or oversight of the IT department. The scale is no longer marginal, as a 2026 Gartner survey of cybersecurity leaders found that 69 percent have evidence or suspect that employees are using public generative AI at work.

What shadow AI is

Shadow AI is the use of AI tools, models, and services by employees without the knowledge, approval, or governance of an organization's IT or security teams. It is a subset of shadow IT, the broader category of hardware and software that IT has not approved, but it carries risks that older shadow IT controls were never designed to handle. The distinction matters for how an organization should respond.

Where shadow IT generally involves an unapproved application or storage service, shadow AI centers on systems that process, generate, and retain data in ways that are difficult to reverse. Common forms include:

• Public chatbots and assistants used on work data, such as the ChatGPT app or Claude Desktop signed in with a personal account.


• AI features inside browser tabs and SaaS products that an employee turns on without review.


• Coding agents in the terminal and IDE, including Claude Code, Codex, and Cursor, that read source code and call external services.


• MCP servers wired into those tools, which can read files, call APIs, and act on a user's behalf.

Salesforce's 2026 Workforce AI Survey found that 67 percent of employees use AI at work, while only 18 percent of organizations have a formal AI security policy. Adoption at that pace, with no governing layer underneath it, is what turns ordinary productivity into a security exposure.

Why shadow AI is a security risk, not just a policy gap

Shadow AI raises measurable security and compliance risk because sensitive data reaches systems that the organization cannot see, control, or audit. Gartner predicts that by 2030, more than 40 percent of organizations will experience built by Maxim AI, is that one place. The gateway already holds the that inspect prompts and responses, and the audit logs that record every exchange. The limitation, until now, has been reach: those controls governed only the traffic that someone had configured to point at the gateway.

follows the same governed path on every machine:

1. A user works in a desktop app, a browser AI surface, or a coding agent exactly as before, with apply to endpoint traffic with no extra setup on the device. A guardrail runs before a prompt reaches a model and again before a response returns, so secrets and personal data are caught or redacted before they leave the machine. Built-in coverage includes Gitleaks-backed secrets detection for leaked API keys, tokens, and credentials, a PII detection template built on custom regex, and content safety, alongside integrations with AWS Bedrock Guardrails, Azure Content Safety, Google Model Armor, CrowdStrike AIDR, GraySwan Cygnal, and Patronus AI.
   
   
   

   
   
   
   Visibility into MCP servers across the fleet
   

   
   
   

   Most organizations cannot say which MCP servers their employees have connected to AI tools. lets administrators decide which AI applications are permitted across the organization. Approved apps run normally, with their traffic governed through Bifrost, while disallowed apps are blocked before any data leaves the machine. When Edge encounters an app or MCP server it has not seen, it requests approval from the admin console, and administrators choose whether pending items are allowed or blocked while a decision is pending. Policy changes reach the whole organization at once, without anyone revisiting individual machines.

   
   
   

   
   
   
   Rollout through your existing device management
   

   
   
   

   and register there for alpha access.

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