A CIO’s Checklist for Bringing Shadow AI into the Light

IT leaders have two choices when it comes to Shadow AI: try to stop the train or get on board. Veteran technology leader and executive IT consultant Christoph Hesterbrink offers a 10-point checklist for CIOs to get a handle on shadow AI without slowing progress.

Shadow IT is nothing new, so the rapid emergence of shadow AI shouldn’t surprise any CIO. As AI capabilities advance, employees are understandably experimenting with tools that help them move faster, serve customers better, and reduce manual work. In most cases, no one is trying to do anything “wrong”; they’re trying to get their jobs done. And attempts to block AI outright usually just push this activity further underground.

For CIOs, the core issue is not defining shadow AI more precisely but accepting that it is a symptom of unsatisfied demand and responding with a clear, pragmatic plan. The objective is not to suppress AI blindly but to enable responsible use while still reaping the benefits of speed and innovation. With the right framing and tools, IT leaders can bring shadow AI into the light and become catalysts for progress instead of perceived obstacles.

Three Types of Shadow AI

Getting a handle on shadow AI starts with recognizing that not all ungoverned AI usage is the same. Each category carries different risks and therefore requires different controls from the IT organization.

The first category is shadow LLM usage. This includes tools such as ChatGPT, Claude, and Perplexity which are used informally for writing, summarization, analysis, and ideation. Almost everyone in the business can be a user, which means the primary risks are data leakage, contractual and intellectual property exposure, and “hallucinated” output that appears authoritative but is wrong. Reasonable controls include concise, single‑page guidance on acceptable use, enterprise LLM licenses to provide a safer default, clear examples of prohibited inputs, and logging to support oversight.

The second category is shadow “vibe coding” or AI‑assisted software development. This happens when application developers and technically-inclined analysts use tools such as Copilot, Codeium, or Replit AI. A major risk is license contamination, which happens when a company mixes its proprietary code with open-source software, especially strong "copyleft" licenses (like general public licenses), and inadvertently makes its own code subject to the same open terms. Another danger is encouraging developers to bypass standard software development lifecycle processes. CIOs can mitigate these risks by approving specific development tools, introducing code provenance scanning, training developers on “never paste” rules for sensitive content, and updating secure coding standards to cover AI‑generated code.

The third category is shadow agentic AI and automated workflows. This use is often seen with platforms like Zapier AI, RPA suites with AI capabilities such as UiPath or Power Automate, or local/open‑source agents.

Typical users are operations, marketing, customer service, and finance teams, who independently deploy these tools to automate workflows that then move data around and make decisions without human oversight. The related risks include uncontrolled data flows, autonomous decision‑making, and fragile processes whose failure can disrupt operations. Appropriate controls include an agentic/automation intake process, defined integration approval workflows, monitoring automation traffic, and explicit red lines on how many autonomous agents users are allowed to deploy simultaneously.

5 Guiding Principles for CIOs

Shadow AI — like any shadow IT — does not arise in isolation; it is often a direct response to how the IT function operates. When employees perceive IT as standing in the way of progress, they work around it, and shadow usage proliferates. A few guiding principles can help CIOs reverse that pattern and become visible champions of responsible AI.

First, move fast: aiming for weeks rather than months to put basic guardrails in place. If governance lags too far behind adoption, informal AI usage will define the organization’s risk profile before IT has a chance to shape it. 

Second, do not over‑govern. Excessive restriction tends to increase risk by driving activity onto unmanaged devices, networks, and accounts.

Third, enlist a small but powerful coalition that includes internal audit, security, legal, HR, and respected business champions to become part of the IT governance process with a focus on AI usage. This group will bring diverse perspectives on risk, policy, and communications, and ensures that AI governance is not seen as “just an IT thing”.

Fourth, clarify everyone’s role. In particular, determine whether IT is acting as an order‑taking service provider or a strategic enabler of responsible AI usage.

Finally, meet people where they are. Different business units need different levels of control and support. A single, rigid model will likely be ignored or quietly bypassed.

A 10‑Point “Stay out of Trouble” Checklist

CIOs need to get their arms around shadow AI now, not in the next budget cycle. The following ten‑point checklist is a pragmatic way to reduce risk while still enabling the business.

1. Publish a one‑page “do”s and “don’t”s list immediately, with separate sections for LLMs, vibe coding tools, and agentic AI so employees understand expectations immediately.
2. Create an enterprise AI sandbox. Even a minimal, compliant alternative can sharply reduce the appeal of completely unsanctioned tools.
3. Define what “confidential data” means using real examples and explain how misuse of that data introduces risk in the contexts of LLMs, AI‑assisted coding, and agentic tools.
4. Create a disclosure channel for AI usage, making it explicit that its purpose is organizational safety, not punishment for experimentation.
5. Run light monitoring for AI‑related traffic, focusing on patterns and hotspots rather than broad blocking of tools.
6. Build a library of pre‑approved use cases by role and by tool category, such as sales‑oriented LLM scenarios, developer coding patterns, and agentic automations for operations.
7. Define an “off‑limits” list for each category of AI (e.g., customer data, contracts, and unreleased roadmaps for LLMs; proprietary algorithms and sensitive integrations for vibe coding; and customer‑impacting actions or cross‑system data transfers for agents).
8. Create a rapid AI tool intake process with specific checklists for LLMs, coding assistants, agentic tools, and data pipelines, countering the perception that going through IT takes too long.
9. Conduct pilots with high‑demand teams, turning the biggest shadow users into allies who can help refine controls and demonstrate value.
10. Establish a quarterly AI governance rhythm to review updates, newly approved tools, risk findings, and the roadmap, signaling that AI oversight is a continuous discipline rather than a one‑time exercise. 

From Shadow AI to “Visible AI”

Organizations that take this approach can significantly shift behavior. One large professional services firm, for example, centralized AI on a common platform, adding compliance guardrails, and emphasized enablement over restriction. As a result, they harnessed the benefits of public LLMs while mitigating the risks. A manufacturer reduced shadow coding tool usage by rolling out an approved AI assistant. And a small nonprofit decreased risk exposure by focusing on policy, “safe task” lists, and clarity for staff. These examples illustrate that visibility and enablement — not blanket bans —are the levers that work in practice.

CIOs cannot stop shadow AI, but they can outpace it with practical guardrails and rapid enablement. With the right posture, shadow AI becomes “visible AI” that the business can use confidently and safely, with IT acting as a partner rather than a gatekeeper, and audit and legal assured that risks are being actively managed. The CIOs who succeed will be those who move quickly, communicate clearly, and reduce friction while keeping the organization firmly on the AI train rather than standing in front of it.