Address shadow AI in the organization: discovering unsanctioned use, assessing the real exposure, providing sanctioned alternatives, and policy that people follow.
Shadow AI governance addresses the AI tools employees already use without approval. In most organizations this has been happening for some time, which makes discovery the first task rather than policy.
The instinct to respond with prohibition usually fails, because the underlying demand is real and blocking one tool moves usage to another.
Understand the actual exposure
The risk is not that people use AI. It is that confidential information leaves the organization into systems with unknown retention, unknown access, and no contractual protection.
Specific exposures worth assessing: customer personal data pasted into consumer tools, source code and internal documents uploaded for analysis, credentials appearing in shared context, regulated data processed outside approved boundaries, and outputs used in decisions with no record of how they were produced.
Assess these concretely for your environment. A general statement that shadow AI is risky does not help prioritize.
Discovery without punishment
You cannot govern what you cannot see, and people conceal usage they expect to be punished for.
Run an amnesty survey asking what tools people use and what problems they solve. Frame it as gathering requirements, not as an audit, and mean it. The results are usually more complete and more useful than network-based discovery alone.
Supplement with technical discovery: outbound traffic to known AI services, browser extension inventories, SaaS integrations connected to your systems, and expense records showing individual subscriptions.
Pay attention to what the tools are being used for. That is your requirements list for a sanctioned alternative.
Provide a sanctioned path quickly
The reliable way to reduce shadow usage is to make the approved option good enough and available soon. Policy alone shifts usage to less visible tools.
Prioritize by what the survey found. If most usage is drafting and summarizing, an approved general assistant with enterprise terms covers the majority. If it is code assistance, that is a separate procurement with its own data questions.
Approved tools need enterprise agreements covering retention, training exclusion, access controls, and audit. Consumer tiers of the same products typically do not offer these, which is precisely the gap being closed.
Make access easy. An approved tool behind a two-week request process loses to an unapproved one available immediately.
Write policy people can follow
Policy that says "do not use AI tools" is ignored. Policy that tells people what they may do, with which data, on which tools, is followed.
Structure it by data classification rather than by tool. State clearly which data classes may be used with approved tools, which may never leave the organization, and what to do when unsure.
Give concrete examples, since most violations come from uncertainty rather than defiance. Someone pasting a customer email into a summarizer usually has not considered it a data transfer.
Provide a fast path for requesting new tools, with a stated turnaround. Without it, shadow usage returns as needs evolve.
Ongoing controls
Maintain an inventory of approved tools with their data terms and owners, and review it as vendors change terms.
Monitor for new services appearing in outbound traffic, and treat spikes as a signal that an unmet need has emerged rather than only as a violation.
Include AI tool use in onboarding, since new joiners arrive with habits from previous employers.
Repeat the survey periodically. Usage patterns shift quickly as new tools appear.
Frequently asked questions
Should we block AI tools at the network level?
Blocking without a sanctioned alternative moves usage to personal devices, where you have no visibility at all. Provide the alternative first.
What is the highest-risk usage to address?
Regulated or customer personal data in consumer tools with retention and training on inputs.
How do we handle employees who already sent confidential data?
Treat it as an incident to assess and contain, and respond in a way that keeps future disclosure likely rather than deterring it.
Does an enterprise agreement remove the risk?
It addresses retention, training, and contractual protection. Data classification rules and access controls remain necessary.
Explore our software and web capabilities or contact Voquarn Code to discuss AI governance in your organization.
Written by
Moueen Togarvi
Founder & CEO at Voquarn Code, focused on product engineering, search growth, and practical AI systems.