Shadow AI: How to reduce the risks of unauthorized AI use
- If your business doesn’t have careful AI governance and policies, you risk shadow AI dangers caused by team members using unauthorized AI tools.
- Shadow AI can result in risks like sensitive business or customer data being shared with public AI models or unsanctioned AI tools being given access to your core systems, leading to financial, regulatory and reputational penalties.
- To protect your business from shadow AI, implement governance and clear AI policies to guide your team in how it uses AI.
As AI continues to transform how many businesses operate, too few executives fully understand the risks that come alongside the rewards. One of the most prominent of those risks is called shadow AI.
Most commonly appearing in organizations that don’t have a strong AI governance structure already in place, shadow AI can expose your business to financial, regulatory and reputational damage. But it’s also a risk you can dramatically reduce or even eliminate by putting thoughtful policies in place and properly training your team.
Keep reading to learn more about what shadow AI is and how to protect against it.
What is shadow AI?
Shadow AI refers to the use of unapproved AI tools by employees within your business. This almost always happens because well-meaning team members are simply trying to do their jobs, but can create substantial business risks, like exposing your internal or customer data to public AI models.
Shadow AI occurs when an organization doesn’t have strong AI governance or an AI use policy in place. Absent such oversight, team members often choose their own AI tools without considering whether those tools meet data privacy, regulatory, anti-bias or cybersecurity requirements.
This far into the AI era, you should assume that many of your team members use AI as part of their daily workflows. That means if you don’t have clear AI policies to govern what tools your team uses or how you protect your data, you are already exposed to the risks of shadow AI.
Why is shadow AI a business risk?
Shadow AI is a significant business risk that can expose an unprepared organization to financial losses, reputational harm, regulatory action and cybersecurity threats. To understand how, consider the following scenario:
Working to meet an urgent deadline, a manager at a financial services firm feeds a large dataset into an AI tool, asking the AI to analyze the data and draw conclusions that she can use in a presentation to her departmental leadership the next day.
So far, seems reasonable, right? Analyzing large datasets is one of AI’s biggest strengths. But here’s the problem: The manager doesn’t realize that she’s picked a tool that uses its inputs for training data — which means it’s now absorbed all the proprietary customer data in the dataset she gave it.
In this scenario, her company’s private customer details could start appearing in answers the AI tool gives to other users. Regulators will be furious, and so will any customers who find out what’s happened, which exposes the manager’s employer to fines, reputational damage and even litigation.
If you want to take this example even further, imagine what could happen if the AI tool had security flaws that could allow hackers to reconstruct the full dataset.
How does shadow AI happen?
Shadow AI happens when an organization’s leadership fails to implement effective AI governance. If you want your employees to use AI in a way that protects your business and mitigates your risks, you need to show them how.
Organizations that adopt AI piecemeal rather than through a cohesive enterprise strategy are typically most at risk for shadow AI. In these cases, shadow AI use typically builds up gradually, as team members turn to AI of their own initiative to solve problems or work faster.
A few examples include:
- Putting internal data into a free chatbot model.
- Connecting an unauthorized AI assistant to company systems.
- Using an AI to make decisions that affect customers or team members.
- Asking a consumer-facing AI to summarize a video of a sensitive client meeting.
Essentially, any scenario where someone on your team gives data to or relies on outputs from an AI that your IT and governance team hasn’t already approved can put your business at risk.
What are the primary risks of shadow AI?
Shadow AI use can create unexpected risks for your business. These can be financial, reputational or regulatory and can also include security risks.
- Financial costs: Shadow AI use can expose your business to fines, litigation and other costs associated with giving internal or customer data to unauthorized AI systems.
- Reputational damages: Customers, clients, partners or vendors whose data is exposed by shadow AI use won’t appreciate that — and neither will anyone else who hears about it.
- Regulatory challenges: AI use that violates regulatory or anti-bias requirements will generate ire from regulators, which can include sanctions, penalties, fines or corrective actions.
- Cybersecurity dangers: Security vulnerabilities within an unsanctioned AI tool could create cybersecurity risks by allowing hackers to gain access to your data or systems.
Also consider that the quality of AI outputs depends heavily on the quality of the data being input. If your teams are using shadow AI tools outside of an organized, enterprise-level AI strategy, you likely don’t have an AI-ready data foundation (like a data lake house) either.
This means that your shadow AI users, which may include executives, run the risk of making business decisions based on unapproved tools operating on poor-quality data.
How to reduce shadow AI risks
To reduce your shadow AI risks and cut down on unauthorized AI use, make AI a pillar of your overall enterprise risk management strategy. Here are key steps to effective AI risk management:
Establish AI governance
Setting up a formal AI governance structure establishes oversight on your team’s AI use and protects your business from shadow AI. Effective governance is an ongoing process that involves choosing a governance team, selecting approved AI tools, writing policies and seeking feedback from other stakeholders.
Crucially, governance is also an opportunity to talk about how your business can use AI to work more efficiently and effectively.
Approve enterprise AI tools
Selecting and implementing specific AI tools for your team to use dramatically reduces your shadow AI risks. This is because enterprise-level AI options typically protect your data in a way that consumer AI products don’t.
Create an AI policy
An AI policy establishes written guidelines for how your team should use AI. Creating clear policies helps your team understand what’s allowed and what isn’t, which helps stop inadvertent shadow AI activities.
Train employees
You can’t expect your employees to follow your AI policies and responsibly mitigate AI-related risks without training. Like cybersecurity, this should be a process, not a one-time event, in order to keep your AI policies top of mind.
Monitor AI use
Your IT team and governance committee should monitor your organization’s AI use and continually assess your risk exposure. AI and AI-related risks are constantly evolving, and your policies and governance will need to, too.
How Wipfli can help
We advise businesses on managing technology risks like shadow AI. Let’s talk about how you can make AI work for your business by choosing the right tools, integrating them into your processes and establishing governance. Start a conversation.
