Solution

Shadow AI: The Risk You Can’t See

Shadow AI is unsanctioned employee use of AI tools and models. Learn how it happens, why traditional controls miss it, and how to build visibility and control.

In short

Shadow AI is the use of AI tools, models, and browser extensions by employees without the security team’s approval or visibility. It spans public assistants such as ChatGPT, Claude, and Gemini, AI features embedded inside SaaS applications, unsanctioned browser extensions, and open-weight models run locally through runtimes like Ollama. The core risk is irreversibility: the moment an employee pastes customer data, source code, or intellectual property into an unsanctioned model, that data has left the organisation and cannot be recalled.

What it gives you

It is already happening
AI adoption is driven bottom-up by individuals, so it precedes any procurement or security review.
Exposure is irreversible
Data submitted to a public model that trains on input cannot be retrieved or deleted after the fact.
It hides from network tools
Off-network laptops and locally-run models generate no traffic for a proxy or CASB to inspect.
Regulators now expect control
GDPR, PDPL, HIPAA, and the EU AI Act all assume you can demonstrate control over AI data flows.

How Shadow AI enters an organisation

Shadow AI is rarely malicious. It is the predictable result of a capable tool being one browser tab away from an employee under deadline pressure:

An engineer pastes a failing stack trace — including a production connection string — into a chatbot to debug it faster.
A sales lead uploads a customer list to summarise it into talking points before a meeting.
A finance analyst pastes a draft earnings table to reformat it, months before the figures are public.
A developer installs a browser extension that silently reads page content, including internal systems.
A team runs an open-weight model locally, believing it is inherently safe because it never leaves the laptop.

Why the usual controls do not work

Most organisations reach first for the tools they already own. Each has a structural gap for this problem specifically:

Blocking the domains pushes usage onto personal devices, removing the last of your visibility.
CASB and SWG see the destination but not the prompt content, and see nothing at all off-network.
Traditional DLP inspects files and network egress, not text pasted into a chat box over TLS.
An acceptable-use policy document changes intent, but produces no telemetry and no enforcement.

A workable path to control

The organisations that handle this well follow the same sequence: measure before you legislate, then enforce gradually.

Start monitor-only. Build a real inventory before writing a single rule.
Classify by data, not by tool. The question is what is being sent, not which logo is on the tab.
Enforce gradually — coach first, redact where you can, block only where you must.
Offer a sanctioned alternative, so the safe path is also the easy one.
Report continuously, so the trend line is visible to leadership and auditors alike.

FAQ

Shadow AI: The Risk You Can’t See: questions we get asked

Straight answers to what security, privacy, and compliance teams ask us first.

What is the difference between Shadow IT and Shadow AI?

Shadow IT is unsanctioned software and services generally. Shadow AI is a subset with a sharper risk profile: the interaction itself is the data leak. Where an unsanctioned file-sharing tool creates the possibility of exposure, an unsanctioned AI prompt transmits the sensitive data as its normal mode of operation, and may embed it in a model that trains on input.

Is running a local model safer than using a public chatbot?

It removes the third-party training risk, but it does not remove the governance problem. A locally-run model still processes sensitive data with no audit trail, no data classification, and no visibility for the security team — and it is invisible to every network-based control, which makes it harder to discover than a public chatbot.

Should we just block AI tools entirely?

Blanket blocking reliably moves usage to personal devices and personal accounts, where you have no visibility and no control at all, while surrendering a real productivity advantage. Graduated enforcement — coaching, redaction, and blocking only the genuinely high-risk interactions — produces better security outcomes and far better compliance from users.

Keep reading

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