How Much Shadow AI Is Hiding in Your Organization?
Fifteen scored questions across six risk dimensions — see where unsanctioned AI is leaking data, bypassing review, and turning your security policy into theatre.
- A scored profile across 6 dimensions — see exactly where you're strong and where the gaps are.
- Your biggest opportunities, mapped to specific next moves.
- A personalized video walkthrough from Shawn (optional) — a real read on your results.
Shadow AI sprawl is the gap between what your security policy says about AI and what your employees are actually doing with it. Industry research from McKinsey and Gartner consistently lands on the same uncomfortable ratio: roughly 78% of knowledge workers report using AI tools regularly, while only about 40% of organizations have any written policy governing that use. That delta is where the real exposure lives. Customer records pasted into consumer chatbots. Source code summarized by personal accounts. Confidential contracts uploaded to free tools whose terms of service grant training rights. None of it shows up on a CISO's dashboard, because none of it was provisioned by IT.
This free shadow AI audit scores your organization across six risk dimensions and returns a clear exposure profile in about five minutes. It's built from 27 years of technology leadership across Fortune 500 and growth-stage companies — the same lens a fractional Chief AI Officer would bring to a first conversation about discovery, policy, and incident response. The framing is deliberately practical: not a maturity model, but a list of the specific gaps an auditor, a regulator, or a journalist would find if they came looking next quarter.
What this shadow AI audit measures
Shadow AI risk is a profile, not a single number. The audit scores six dimensions independently so you can see where you're exposed: Visibility & Discovery (can you list every AI tool actually in use), Policy & Acceptable Use (do employees know what's off-limits), Data Exposure (is confidential data leaking via AI tools), Vendor AI Sprawl (have your existing SaaS vendors quietly turned on AI features), Identity & Access (can you tell who is using what tool with which credentials), and Incident Detection & Response (would you actually catch a leak). The final question maps the specific departments — engineering, marketing, sales, HR, finance, legal — where you most suspect shadow AI is already happening.
Why shadow AI sprawl is a board-level risk
Boards are starting to ask CISOs and general counsel for explicit answers about AI exposure, and most existing security postures weren't built to answer them. Three forces compound: consumer AI tools are free, frictionless, and embed in browsers; existing SaaS vendors are quietly shipping AI features that default on at renewal; and regulators in the EU, US states, and sector-specific bodies are publishing AI-use disclosure rules with real enforcement teeth. The result is that organizations whose discovery, policy, and audit trail don't reach into AI tool usage are carrying a contingent liability they cannot quantify. A shadow AI audit makes the liability visible so it can be priced, contained, and reported to the board.
What you get at the end
You'll see an overall shadow AI risk score, a band that describes where you stand (from Severe Exposure through Strong Governance Posture), a per-dimension breakdown, and a department-by-department suspicion map. From there you can request a personalized video walkthrough — a short, recorded read on your specific results and what a fractional Chief AI Officer engagement would do to close the gaps. No generic sales deck.
Frequently asked questions
What is shadow AI?
Shadow AI is the use of AI tools — consumer chatbots, IDE assistants, AI features inside SaaS platforms — without explicit IT or security approval. It's the AI equivalent of shadow IT, with one important difference: every AI interaction can carry data outside the organization's perimeter, and most consumer terms of service allow that data to be used for model training. Shadow AI is what most employees mean when they say their company hasn't done much with AI yet.
How is this different from a normal AI policy review?
A typical AI policy review asks whether you have a written policy. This audit asks whether the policy actually constrains behavior — whether you can see what's in use, whether employees know the rules, whether DLP and SSO actually cover the paths people take, and whether you'd catch a leak if one happened. It's the gap between paper compliance and operational reality.
How long does the audit take?
About five minutes. Fifteen scored questions across six risk dimensions, plus two financial-context questions and a department-by-department suspicion map. Your progress auto-saves, so you can pause and resume without losing answers.
Who should take this assessment?
CISOs, CIOs, general counsel, heads of risk, and CEOs who want a defensible read on AI exposure before a board meeting, an audit, a regulator inquiry, or a renewal cycle. It's also useful for IT and security leaders building the business case for AI governance investment.
What if I score badly?
A low score isn't a verdict — it's a map. Most organizations score in the lower bands right now, which is exactly why this is the moment to act. The follow-up walkthrough turns the score into a sequenced remediation plan: what to fix first, what to fix in the next 90 days, and what can wait. Knowing the order matters more than the number.