Understanding Shadow AI and its impact on your business

The market thrives on innovation and new AI projects. It’s no wonder companies are rushing to use AI to stay ahead in today’s fast-paced economy. However, this rapid adoption of artificial intelligence also creates a hidden problem: the emergence of “Shadow AI.’

Here’s what AI does in everyday life:

• Time savings thanks to the automation of repetitive tasks.
• Generating ideas that once took a long time to uncover.
• Improving the decision-making process using predictive models and data analysis.
• Content creation using AI tools for marketing and customer service.

All of these benefits explain why businesses 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 an organization’s IT or security team.

Although at first it may seem harmless or even beneficial, such unregulated use of artificial intelligence can expose various risks and threats.

finished 60% of employees acknowledge the use of unauthorized artificial intelligence tools for work-related tasks. This is a significant percentage when you consider the 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.

In Shadow IT, employees use unauthorized hardware, software or services. Shadow AI, on the other hand, focuses on the unauthorized use of AI tools to automate, analyze or improve work. This may seem like a shortcut to faster and smarter results, but it can quickly lead to problems if not properly controlled.

Risks associated with Shadow AI

Let’s take a look at the risks of shadow AI and discuss why it’s important to maintain control over your organization’s AI tools.

Breach of data privacy

Use of unapproved AI tools may compromise data privacy. Employees may accidentally share sensitive information while working with unverified applications.

each one every fifth company in the UK faced a data breach due to employee use generative AI tools. A lack of proper encryption and oversight increases the likelihood of data leakage, leaving organizations open to cyber attacks.

Non-compliance with regulatory requirements

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

Failure to comply can result in large fines. For example, GDPR violations can cost companies up to 20 million euros, or 4% of their global income.

Operational risks

Shadow AI can create a mismatch between the results produced by these tools and the goals of the organization. Overreliance on untested models can lead to decisions based on unclear or biased information. This misalignment can impact strategic initiatives and reduce overall operational efficiency.

In fact, a poll found that nearly half of senior executives are concerned about the impact of AI-generated disinformation on their organizations.

Damage to the reputation

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

A striking example is the backlash against Sports Illustrated when discovered, they were using AI-generated content with fake authors and profiles. The incident highlighted the risks of poorly managed use of artificial intelligence and sparked debate about its ethical impact on content creation. This highlights how a lack of regulation and transparency in AI can undermine trust.

Why Shadow AI is becoming more common

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

• Lack of awareness: Many employees are not aware of the company’s policy regarding the use of AI. They may also be unaware of the risks associated with unauthorized tools.
• Limited organizational resources: Some organizations do not provide proven AI solutions that meet the needs of employees. When approved solutions are ineffective or unavailable, employees often look to external options to meet their requirements. A lack of adequate resources creates a gap between what the organization provides and what teams need to perform effectively.
• Inappropriate stimuli: Organizations sometimes prioritize immediate results over long-term goals. Employees can bypass formal processes to achieve quick results.
• Using free tools: Employees can find free AI programs online and use them without notifying IT. This may lead to unregulated use of confidential data.
• Update existing tools: Teams can enable AI features in approved software without permission. This can create security gaps if these features require security checks.

Manifestations of Shadow AI

Shadow AI appears in various forms in organizations. Some of them include:

Chatbots based on AI

Customer service teams sometimes use unverified chat bots to process requests. For example, an agent may rely on a chatbot to prepare responses rather than referring to company-approved instructions. This could lead to inaccurate reporting and disclosure of confidential customer information.

Machine learning models for data analysis

Employees can upload their own data to free or external machine learning platforms to discover insights or trends. A data analyst may use an external tool to analyze customer buying patterns, but unknowingly put sensitive data at risk.

Marketing automation tools

Marketing departments often use unauthorized tools to optimize tasks, such as email campaigns or engagement tracking. These tools can improve productivity, but they can also mishandle customer data, violating compliance regulations and undermining customer trust.

Data visualization tools

AI-powered tools are sometimes used to create quick dashboards or analytics without IT permission. Although effective, these tools can generate inaccurate data or compromise sensitive business data if used carelessly.

Shadow AI in generative AI programs

Teams often use tools like ChatGPT or DALL-E to create marketing materials or visual content. Left unattended, these tools can create misbranded messages or raise intellectual property concerns, creating potential reputational risks for an organization.

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 should define a clear policy for the use of AI within the organization. These policies should outline acceptable practices, data processing protocols, privacy measures and compliance requirements.

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

Classify data and use cases

Companies should classify data based on its sensitivity and importance. Critical information such as trade secrets and personally identifiable information (PII) should receive the highest level of protection.

Organizations must ensure that public or unverified cloud AI services never process sensitive data. Instead, companies must rely on enterprise-grade AI solutions to ensure high data security.

Acknowledge benefits and offer guidance

It is also important to recognize the benefits of shadow AI, which often arise from a desire to improve efficiency.

Instead of prohibiting its use, organizations should guide employees to implement AI tools within a controlled system. They must also provide approved alternatives that meet performance needs while ensuring safety and compliance.

Train and educate employees

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

Educated workers are more likely to use AI responsibly, minimizing potential security and compliance risks.

Monitor and control the use of AI

Equally important is tracking and controlling the use of AI. Companies should implement monitoring tools to monitor AI applications across the organization. Regular checks can help them identify unauthorized tools or security gaps.

Organizations should also take preventative measures, such as analyzing network traffic, to detect and remediate abuse before it escalates.

Collaborate with IT and business units

Collaboration between IT and business teams is vital to choosing AI tools that meet organizational standards. Business units should have a say in the choice of tools to ensure practicality, while IT ensures compliance and security.

Such teamwork promotes innovation without compromising the security and operational goals of the organization.

Steps forward in the ethical management of AI

As reliance on AI increases, clear and controlled management of shadow AI may be key to remaining competitive. The future of artificial intelligence will depend on strategies that align organizational goals with the ethical and transparent use of technology.

For more on how to manage AI ethically, stay tuned Unite.ai for the latest information and advice.