How to Govern Shadow AI Without Blocking Your Teams
A practical, security-toned playbook for CISOs, CIOs, and platform owners: seven steps to find unsanctioned AI and bring it under governance by routing every sanctioned model call through one path you control. With the self-hosted option, prompts and provider keys never leave your cloud.
The short version
How do I govern shadow AI?
Find it first, then give it one governed path. Discover which AI tools people use, then route sanctioned AI through Behest, the control plane for enterprise AI, with model allowlists, an audit trail, and budgets. PII scrubbing and prompt-injection defense are on the Enterprise plan, and self-hosting is optional.
Why shadow AI is a spend and security problem
Shadow AI is what happens when adoption outruns governance. A team wires an app straight to a provider, an analyst pastes a customer list into a personal AI tab, an agent spins up on an unvetted model. None of it is sanctioned, and all of it is invisible. Each path is a place sensitive data can leave, a prompt can be hijacked, and spend can accrue without a budget or an owner.
Regulatory pressure is rising alongside it, with the EU AI Act pushing organizations to document and control how AI handles data. Behest doesn't hand you a compliance certificate. It gives you the controls that governance rests on: model allowlists, an audit trail, budgets, and self-hosting as an option, with PII scrubbing and prompt-injection defense on the Enterprise plan. The fix is to make every sanctioned model call take one governed path instead of many ungoverned ones.
Not sure how much shadow AI you have? Start by finding it. Behest Radar is a free, observe-only app that shows which AI services a machine is using, including coding assistants and command-line tools that never touch a central endpoint. The shadow AI discovery overview covers the other places to look.
Govern shadow AI in seven steps
Find it first. Every later step maps to a control Behest runs on the request path, in our cloud or yours.
- 1
Discover the shadow AI you already have
Before you set policy, find out what is in use. Tell people what you are checking first. Pull OAuth grants, expense records, and network logs, then check developer machines, where coding assistants and command-line tools call AI providers directly. Behest Radar is a free, observe-only app that shows which AI services a machine is using.
- 2
Route sanctioned AI through one governed path
Point every approved app, script, and team at a single OpenAI-compatible endpoint instead of letting each one call providers directly. One governed path is what makes the rest possible: you can't allowlist, scrub, or audit calls you can't see.
- 3
Set a model allowlist of approved providers
Restrict traffic to the models and providers you've approved, so a team can't quietly route sensitive data to an unvetted endpoint. New models pass review before they're reachable, rather than after they've already processed your data.
- 4
Scrub PII before it reaches the model
On the Enterprise plan, every prompt runs through PII scrubbing (Presidio-based), so detected PII such as names, emails, and other sensitive fields is redacted before it leaves your environment for a provider. Sensitive data is handled on the request path, not left to each app to remember.
- 5
Screen every call for prompt injection
On the Enterprise plan, Sentinel screens inbound prompts for injection and jailbreak attempts, making it far harder for an attacker to smuggle instructions through a user input field. The check runs on the request path for every call, not as an after-the-fact log review.
- 6
Keep an audit trail of every request
Record who called which model, when, and at what cost, so every AI request has an auditable record. That trail is the evidence base a governance or security review needs, and it exists whether or not the app thought to log it.
- 7
Meter, attribute, and budget the now-visible usage
Once every sanctioned call flows through one path, AI Token FinOps, Behest's cost-control layer, meters it, attributes the cost to the team, project, or user behind it, and enforces token and dollar budgets. Shadow spend becomes visible, owned, and capped instead of surfacing on the provider invoice.
Frequently asked questions
- What is shadow AI, and why is it a governance risk?
- Shadow AI is any AI tool or model call your teams use that IT never sanctioned: a personal ChatGPT tab, an app wired straight to a provider, an agent nobody signed off on. It is a risk on two fronts: spend accrues with no budget or owner, and sensitive data can reach an unvetted model with no audit trail. You cannot govern what you cannot see.
- How do I find shadow AI before I govern it?
- Look in several places: OAuth grants in your identity provider, expense records, network or DNS logs, browser extensions, SaaS admin consoles, and the endpoints themselves. Developer machines often matter most, because coding assistants and CLI tools call AI providers directly. Behest Radar is a free, observe-only app for the endpoint layer that also sees network connections and which browsers reach AI sites. Tell people what you are checking first.
- Can I block unapproved AI models without blocking my teams?
- Yes. That is the point of a governed gateway rather than a firewall ban. Teams keep calling AI through one OpenAI-compatible endpoint; Behest enforces a model allowlist so only approved providers are reachable through Behest, and everything else is simply not an option. People get the AI they need on paths you have vetted, instead of routing around a block you cannot see.
- Does routing AI through a gateway expose or store our prompts?
- Behest runs as SaaS or self-hosted. For shadow AI governance in regulated environments, the Enterprise self-hosted option runs in your own cloud or VPC, so prompts, completions, and provider keys never leave your environment and the audit trail lives in your infrastructure. That is governance without centralizing your data on someone else's servers.
Bring shadow AI onto one governed path
See how Behest routes every sanctioned model call through allowlists, budgets, and an audit trail, with PII scrubbing and prompt-injection defense on the Enterprise plan. SaaS, or self-hosted in your own cloud.