The market is booming thanks to innovation and new AI projects. It’s no surprise that businesses are rushing to use AI to stay ahead in today’s fast-paced economy. However, this rapid adoption of AI also presents a hidden challenge: the emergence of “Shadow AI.’
Here’s what AI does in everyday life:
- Save time by automating repetitive tasks.
- Generate insights that once took a long time to discover.
- Improve decision-making through predictive models and data analysis.
- Create content via AI tools for marketing and customer service.
All of these benefits clearly show why businesses are keen to adopt AI. But what happens when AI starts operating 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 verified by the organization’s IT or security teams.
Although it may seem harmless or even useful at first, this unregulated use of AI can expose various risks and threats.
On 60% of employees admit to using unauthorized AI tools for work-related tasks. This is a significant percentage when you consider the potential vulnerabilities hidden in the shadows.
Shadow AI vs. Shadow IT
The terms Shadow AI and Shadow IT may sound like similar concepts, but they are distinct.
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. This may seem like a shortcut to faster, smarter results, but it can quickly escalate into problems without proper oversight.
Risks associated with Shadow AI
Let’s examine the risks of shadow AI and discuss why it’s essential to maintain control over your organization’s AI tools.
Data Privacy Violations
Using unapproved AI tools can put data privacy at risk. Employees may accidentally share sensitive information when working with unapproved applications.
Each one in five companies in the United Kingdom faced a data breach due to employee use generative AI tools. Lack of proper encryption and monitoring increases the risks of data breaches, leaving organizations vulnerable to cyberattacks.
Regulatory non-compliance
Shadow AI poses serious compliance risks. Organizations must comply with regulations such as GDPR, HIPAA, and EU AI law to ensure data protection and ethical use of AI.
Non-compliance can result in heavy fines. For example, GDPR violations can cost businesses up to 20 million euros or 4% of their overall turnover.
Operational risks
Shadow AI can create a disconnect 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 effectiveness.
In fact, a investigation reported that nearly half of senior leaders are concerned about the impact of AI-generated misinformation on their organization.
Damage to reputation
The use of shadow AI can damage an organization’s reputation. Inconsistent results obtained with these tools can damage trust between customers and stakeholders. Ethical lapses, such as biased decision-making or misuse of data, can further harm public perception.
A clear example is the reaction against Sports Illustrated when discovered, they were using AI-generated content with fake authors and profiles. This incident showed the risks of mismanaged use of AI and sparked debates about its ethical impact on content creation. It highlights how the lack of regulation and transparency in AI can undermine trust.
Why Shadow AI is becoming more common
Let’s review the factors driving the widespread use of shadow AI in organizations today.
- Lack of awareness: Many employees are unaware of company policies regarding the use of AI. They may also ignore the risks associated with unauthorized tools.
- Limited organizational resources: Some organizations do not provide vetted AI solutions that meet employee needs. When approved solutions are not sufficient or unavailable, employees often look for external options to meet their needs. This lack of adequate resources creates a gap between what the organization provides and what teams need to work effectively.
- Misaligned incentives: Organizations sometimes prioritize immediate results over long-term goals. Employees can bypass formal processes to achieve quick results.
- Using free tools: Employees can discover free AI applications online and use them without informing IT. This can lead to unregulated use of sensitive data.
- Upgrading existing tools: Teams can enable AI features in approved software without authorization. This can create security vulnerabilities if these features require security review.
Shadow AI Manifestations
Shadow AI appears in multiple forms within organizations. Some of them include:
AI-powered chatbots
Customer service teams sometimes use chatbots to process requests. For example, an agent may rely on a chatbot to compose responses rather than referring to company-approved guidelines. This can lead to inaccurate messages and the disclosure of sensitive customer information.
Machine learning models for data analysis
Employees can upload proprietary data to free or external machine learning platforms to discover insights or trends. A data analyst may use an external tool to analyze customer purchasing habits, but unknowingly put confidential data at risk.
Marketing Automation Tools
Marketing departments often adopt unauthorized tools to streamline tasks, like email campaigns or engagement tracking. These tools can improve productivity, but can also mismanage customer data, violating compliance rules and damaging customer trust.
Data visualization tools
AI-based tools are sometimes used to create dashboards or quick analyzes without IT approval. While effective, these tools can generate inaccurate information or compromise sensitive business data if used carelessly.
Shadow AI in generative AI applications
Teams frequently use tools like ChatGPT or DALL-E to create marketing materials or visual content. Left unchecked, these tools can produce off-brand messaging or raise intellectual property issues, posing potential reputational risks to the organization.
Managing the Risks of Shadow AI
Managing shadow AI risks requires a focused strategy emphasizing visibility, risk management, and informed decision-making.
Establish clear policies and guidelines
Organizations must set clear policies for the use of AI within the organization. These policies should describe 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
Businesses must classify data based on sensitivity and importance. Critical information, such as trade secrets and personally identifiable information (PII), must be afforded the highest level of protection.
Organizations should ensure that public or unverified cloud AI services never process sensitive data. Instead, businesses should rely on enterprise-grade AI solutions to ensure strong data security.
Recognize the benefits and offer advice
It is also important to recognize the benefits of shadow AI, which often arises from a desire for increased efficiency.
Instead of prohibiting its use, organizations should guide their employees in adopting AI tools in a controlled setting. They must also offer approved alternatives that meet productivity needs while ensuring security and compliance.
Educate and train employees
Organizations should prioritize employee training to ensure safe and effective use of approved AI tools. Training programs should focus on practical advice so employees understand the risks and benefits of AI while following appropriate protocols.
Educated employees are more likely to use AI responsibly, minimizing potential security and compliance risks.
Monitor and control AI usage
Monitoring and controlling the use of AI is equally important. Businesses should implement monitoring tools to keep tabs on AI applications across the organization. Regular audits can help them identify unauthorized tools or security vulnerabilities.
Organizations should also take proactive steps, such as analyzing network traffic, to detect and address abuse before it escalates.
Collaborate with IT and business units
Collaboration between IT and business teams is essential to select AI tools that meet organizational standards. Business units must have a say in the choice of tools to ensure practicality, while IT ensures compliance and security.
This teamwork promotes innovation without compromising the security or operational objectives of the organization.
Steps forward in the ethical management of AI
As reliance on AI increases, managing shadow AI with clarity and control could be key to remaining competitive. The future of AI will rely on strategies that align organizational goals with ethical and transparent use of the technology.
To learn more about how to manage AI ethically, stay tuned Unite.ai for the latest information and advice.
