Understanding Shadow AI and Its Impact on Your Business

The market is booming with innovation and new AI projects. It’s no surprise that companies are rushing to use AI to stay ahead in today’s fast-paced economy. However, this rapid adoption of AI also brings a hidden challenge: the rise of ‘Shadow AI.’

This is what AI does in everyday life:

• Save time by automating repetitive tasks.
• Generating insights that were previously time-consuming to discover.
• Improving decision making with predictive models and data analysis.
• Creating content through AI tools for marketing and customer service.

All these benefits make it clear why companies are eager to adopt AI. But what happens when AI starts working in the shadows?

This hidden phenomenon is known as Shadow AI.

What do we mean by shadow AI?

Shadow AI refers to the use of AI technologies and platforms that have not been approved or vetted by the organization’s IT or security teams.

While it may seem harmless or even useful at first, this unregulated use of AI can expose several risks and threats.

About 60% of employees admitting to using unauthorized AI tools for work-related tasks. That’s a significant percentage when you consider potential vulnerabilities lurking in the shadows.

Shadow AI vs. Shadow IT

The terms Shadow AI and Shadow IT may sound like similar concepts, but they are different.

Shadow IT involves employees using unapproved hardware, software or services. On the other hand, Shadow AI focuses on the unauthorized use of AI tools to automate, analyze or improve work. It may seem like a shortcut to faster, smarter results, but without proper supervision it can quickly lead to problems.

Risks associated with shadow AI

Let’s explore the risks of shadow AI and discuss why it’s critical to maintain control of your organization’s AI tools.

Data privacy violations

Using unapproved AI tools can compromise data privacy. Employees can accidentally share sensitive information while working with unvetted applications.

Each one in five companies in Britain has suffered data breaches as workers use generative AI tools. The lack of proper encryption and supervision increases the risk of data leaks, making organizations susceptible to cyber attacks.

Non-compliance with regulations

Shadow AI poses serious compliance risks. Organizations must follow regulations such as GDPR, HIPAA, and the EU AI Act to ensure data protection and ethical AI use.

Failure to comply can lead to high fines. For example, violations of the GDPR can cost companies up to €50 €20 million or 4% of their global turnover.

Operational risks

Shadow AI can cause a misalignment between the results generated by these tools and the organization’s goals. Overreliance on unverified models can lead to decisions based on unclear or biased information. This misalignment can impact strategic initiatives and reduce overall operational efficiency.

In fact one questionnaire indicated that nearly half of senior leaders are concerned about the impact of AI-generated disinformation on their organizations.

Reputational damage

Using shadow AI can damage an organization’s reputation. Inconsistent results from these tools can damage trust between customers and stakeholders. Ethical breaches, such as biased decision-making or data misuse, can further damage public perception.

A clear example is the counter-reaction Sports illustrated when it was discovered that they were using AI-generated content with fake authors and profiles. This incident highlighted the risks of poorly managed AI use and sparked debates about its ethical impact on content creation. It highlights how a lack of regulation and transparency in AI can damage trust.

Why Shadow AI is becoming more common

Let’s take a look at the factors behind the widespread use of shadow AI in organizations today.

• Lack of awareness: Many employees are unaware of the company’s policies regarding AI use. They may also be unaware of the risks associated with unauthorized tools.
• Limited organizational resources: Some organizations don’t offer approved AI solutions that meet employee needs. When approved solutions fall short or are unavailable, employees often look to external options to meet their demands. This lack of adequate resources creates a gap between what the organization offers and what teams need to work efficiently.
• Misaligned stimuli: Organizations sometimes prioritize immediate results over long-term goals. Employees can bypass formal processes to achieve quick results.
• Use of free tools: Employees can discover and use AI applications online for free without notifying IT departments. This can lead to unregulated use of sensitive data.
• Upgrade existing tools: Teams can enable AI features in approved software without permission. This could cause security holes if these features require a security review.

Manifestations of shadow AI

Shadow AI appears in multiple forms within organizations. Some of these include:

AI-powered chatbots

Customer service teams sometimes use unapproved chatbots to handle queries. For example, an agent can rely on a chatbot to compose responses instead of referring to company-approved guidelines. This can lead to inaccurate messages and the exposure of sensitive customer information.

Machine Learning models for data analysis

Employees can upload proprietary data to free or third-party machine learning platforms to discover insights or trends. A data analyst may use an external tool to analyze customer purchasing patterns but unknowingly compromise confidential data.

Marketing automation tools

Marketing departments often use unauthorized tools to streamline tasks, such as email campaigns or engagement tracking. These tools can improve productivity, but they can also mishandle customer data, violate compliance rules, and damage customer trust.

Data visualization tools

AI-based tools are sometimes used to create quick dashboards or analyzes without IT department approval. While they provide efficiency, if used carelessly, these tools can generate inaccurate insights or compromise sensitive business data.

Shadow AI in generative AI applications

Teams often use tools like ChatGPT or DALL-E to create marketing materials or visual content. Left unattended, these tools can produce off-brand messages or raise intellectual property issues, posing potential risks to the organization’s reputation.

Managing the risks of shadow AI

Managing the risks of shadow AI requires a focused strategy that emphasizes visibility, risk management, and informed decision-making.

Establish clear policies and guidelines

Organizations must define clear policies for the use of AI within the organization. This policy should outline acceptable practices, data processing protocols, privacy controls, and compliance requirements.

Employees should also learn the risks of unauthorized AI use and the importance of using approved tools and platforms.

Classify data and use cases

Companies must classify data based on its sensitivity and significance. Critical information, such as trade secrets and personally identifiable information (PII), should be given the highest level of protection.

Organizations should ensure that public or unverified AI cloud services never handle sensitive data. Instead, companies must rely on enterprise-level AI solutions to provide strong data security.

Recognize the benefits and offer guidance

It’s also important to recognize the benefits of shadow AI, which often comes from the desire for greater efficiency.

Rather than banning their use, organizations should guide employees in adopting AI tools within a controlled framework. They must also provide approved alternatives that meet productivity needs while ensuring safety and compliance.

Educate and train employees

Organizations must prioritize employee training to ensure the safe and effective use of approved AI tools. Training programs should focus on practical guidance so that employees understand the risks and benefits of AI while following proper protocols.

Trained employees are more likely to use AI responsibly, minimizing potential security and compliance risks.

Monitor and manage AI usage

Tracking and monitoring AI usage is just as important. Companies should implement monitoring tools to keep an eye on AI applications across the organization. Regular audits can help them identify unauthorized tools or security holes.

Organizations should also take proactive measures, such as network traffic analysis, to detect and address abuse before it escalates.

Collaborate with IT and Business Units

Collaboration between IT and business teams is critical to selecting AI tools that align with organizational standards. Business units should have a say in the selection of tools to ensure usability, while IT ensures compliance and security.

This teamwork promotes innovation without compromising the organization’s safety or operational objectives.

Steps forward in ethical AI management

As reliance on AI grows, managing shadow AI in a clear and controlled manner could be key to staying competitive. The future of AI will depend on strategies that align organizational goals with ethical and transparent technology use.

To learn more about how to manage AI ethically, follow Unite.ai for the latest insights and tips.