Shadow AI
Shadow AI is the use of AI models, tools, or APIs inside an organization without IT or security approval or oversight.
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Shadow AI is the AI-era version of shadow IT: employees and teams adopting AI models, browser extensions, API keys, and agent frameworks on their own, outside any approved process. It usually starts with good intentions — someone wants to ship faster — but it leaves the organization unable to see what data is going to which model, or what it costs.
The risk is threefold. There is a cost dimension (untracked provider spend that only surfaces on the invoice), a data dimension (prompts and completions carrying PII or trade secrets to unvetted endpoints), and a compliance dimension (models that never passed legal or security review handling regulated data).
Bringing shadow AI into the light
You cannot govern what you cannot see. Behest gives shadow AI a controlled front door: route requests through one endpoint, and every call is attributed, budgeted, and policy-checked. Model allowlists block unapproved models at the gateway, PII scrubbing protects sensitive data, and the audit trail records what actually happened — converting unmanaged shadow usage into governed, costed usage.
Frequently asked questions
What are model allowlists?
Model allowlists let administrators strictly define which LLMs can be used by which applications or departments. If a developer tries to call an unapproved model (e.g., an experimental model that hasn't passed legal review), Behest blocks the request at the gateway level, ensuring enterprise-wide policy enforcement.