Shadow AI and AI governance: how to manage risk without slowing innovation

What is shadow AI?

Shadow AI refers to the use of generative AI tools, large language models (LLMs), AI agents, or automation workflows inside an organisation without formal approval, oversight, or integration into an AI governance framework.

Shadow AI introduces unapproved intelligence - models, prompts, datasets and workflows that can silently influence decisions and outputs, which presents a significant risk exposure to organisations.

Many high-profile cases have already shown how employees can unintentionally expose sensitive data through public AI tools. The risk is no longer hypothetical - it is real and happening now.

According to Microsoft and LinkedIn’s Work Trend Index, 75% of knowledge workers¹ globally use generative AI at work. Research from Salesforce shows that many employees are doing so without formal approval or guidelines.

While AI adoption is now widespread, the maturity of AI governance practices lags behind. The gap between AI use and its governance is where shadow AI forms, and where new forms of technical debt² begin to accumulate.

Practical AI governance framework

The solution is not to ban AI use, that horse has already bolted! The aim is to move from carrying invisible risk to managing it through active and observable governance.

Below is our practical framework that organisations can use to start their AI Governance journey and address shadow AI.

1. Create visibility before control

Before implementing policies, map current AI usage. Take a note of what tools are being used (ChatGPT, Copilot, Claude, browser extensions), what information and data is being entered into those tools, which business processes now rely on AI-generated outputs, and where those outputs are stored. Larger organisations may require teams to complete a standardised, confidential assessment to get a clear view of AI usage across the company.

You cannot govern what you cannot see.

2. Extend existing data governance to cover AI

An effective AI governance framework should include:

• Clear AI use guidelines
• Rules about what types of data employees are allowed (and not allowed) to put into AI tools
• Alignment with GDPR and sector regulations
• Defined ownership of AI-enabled processes
• Escalation routes for high-risk use cases

AI governance should not be a separate initiative - it should be an extension of existing governance disciplines. Shadow AI often exposes a deeper issue: weak data governance. 

According to Gartner, many organisations are experimenting with AI faster than they are formalising governance frameworks - creating structural weaknesses over time. AI governance is not a one off process, it needs to evolve in parallel with your AI adoption. 

3. Standardise AI tools and ownership

If employees are using unsanctioned tools, it is usually because approved tools are harder to access, policies are unclear, or the productivity gains are too valuable to ignore.

Using the map of AI usage you created in step one, highlight processes and teams where shadow AI has snuck in. Based on your AI governance framework, assigned responsible parties should now suggest enterprise-grade AI tools that would meet the needs of those processes. You now have replaced risky shadow AI with approved platforms that have data protection controls, audit logging, role-based access and clear user guidance.

Make responsible AI adoption simpler than its misuse.

4. Treat prompts and AI workflows as assets

To maintain visibility, productivity, knowledge sharing and consistency across AI-enabled processes, document prompt structures that perform reliably, along with the data sources they rely on. Maintain a clear record of which AI model and version is used for each workflow to ensure outputs don’t change unpredictably and valuable internal knowledge doesn’t disappear when people leave.

Research from McKinsey & Company suggests generative AI can drive 20–40% productivity gains in certain tasks, but without documentation, those gains are fragile and hard to operationalise.

5. Address AI security risks and emerging attack vectors

Shadow AI increases the attack surface. The OWASP LLM Top 10 outlines risks such as:

• Prompt injection
• Data leakage
• Model supply chain vulnerabilities

AI governance must include security review and monitoring - not just policy documentation. Put in place a continuous cycle of AI security guidance and feedback with your IT/CISO teams.

6. Invest in AI literacy

Employees are not trying to create risk, they are trying to work more effectively. However, research from Cyberhaven found that confidential data is already being pasted into public AI tools, often unintentionally. Your AI governance should not just be words on a page, but translate to practical guidance and advice for your employees. 

This should cover:

• What data can and cannot be put into an AI tool e.g. data quality, confidentiality
• How to critically evaluate AI outputs e.g. bias, ethical implications, accountability
• How and where to document AI-assisted processes e.g. humans-in-the-loop
• When and to whom high-risk use cases are escalated

Governance should enable responsible use through clarity, training and feedback.

The strategic perspective

Shadow AI emerges when AI adoption outpaces governance maturity. As generative AI tools become embedded in everyday work, unmanaged usage introduces hidden risk across data protection, security and compliance.

The answer is not restriction but structured enablement: create visibility, extend existing governance disciplines to cover AI, introduce tools, formalise workflows and invest in literacy. After establishing visibility into your AI usage, you can then assess ROI (return on investment).

AI governance is not about slowing innovation. It is about ensuring that innovation remains secure, compliant, sustainable - and ultimately, responsible.

¹ A knowledge worker is a professional who "thinks for a living," using expertise, analysis, and creativity to solve complex problems, develop products, or manage information rather than performing manual labor.

² Technical debt is the accumulated cost and risk created when organisations choose short-term solutions over long-term architectural integrity.