Unmasking Shadow AI: What Is it and How Can You Manage it?

Since the launch of ChatGPT in late 2022, gen AI (generative artificial intelligence) has transformed nearly every facet of our lives, including our professions and workplace environments. Adoption has been driven by employees looking for faster, better ways to perform. For example, applications like ChatGPT, DALL-E, and Jasper are helping employees across industries boost productivity, overcome roadblocks, and brainstorm creative solutions. In fact, in a recent report, Microsoft estimates that 75% of knowledge workers have already adopted gen AI tools into their work. 

But there’s a catch: most of this AI usage is unauthorized by security teams. Just as the rise of shadow AI once disrupted organizations by introducing unapproved software, Internet of Things devices, and cloud-based services, shadow AI threatens to do the same, potentially with more devastating consequences. 

Employees using generative AI risk unintentionally disclosing confidential data, infringing on copyrights, producing inaccurate or biased outputs, and over-relying on AI-generated information. This reliance can potentially put their employers at risk and harm the organization's reputation.

However, there is a path forward where CISOs and CIOs can continue to allow employees to harness generative AI without significantly increasing their organization’s likelihood of suffering a data breach. In this article, we’ll explore shadow AI in more detail, providing an overview of what it is and how to properly manage it in the workplace. 

What Is Shadow AI? 

Shadow AI is the use of unauthorized AI technologies that circumvent IT controls and data protection procedures. For example, a sales employee might use a large language model (LLM) to draft an email response to a client. This unauthorized use of AI presents various cybersecurity concerns, especially if the employee happens to upload sensitive data or any other critical information into an AI solution.

Shadow AI Vs. Shadow IT

Shadow IT is the use of ANY unauthorized software, hardware, or app that circumvents IT controls or an organization’s data protection procedures. This definition includes unauthorized AI. Therefore, shadow AI can be considered a specific form of shadow IT.

The Rise of Gen AI in the Workplace

Generative AI applications have firmly permeated the modern workplace, and guess what? It definitely seems like they’re here to stay. If you look at the underlying numbers, the growth of generative AI in professional environments has been extraordinary, with 46% of employees beginning to use these tools since the start of 2024.

What’s even more interesting (and essential for organizations to understand) is what’s driving this growth. Employees, not corporate mandates, are the leading catalyst for generative AI adoption in the workplace. In other words, generative AI is an employee-led revolution. Workers, including many of your organization’s top producers, are already proactively using AI to boost their productivity and overall impact. Employees want AI at work, and they’re not waiting for companies to give the go-ahead. 

The Double-Edged Sword of AI Adoption

While generative AI can present substantial benefits in professional environments, it also introduces significant security risks, especially when it’s used outside an organization’s standard controls and practices. Data security and inaccurate hallucinations are two of the leading concerns surrounding the use of generative AI in professional environments. 

• Data security concerns: When employees use AI systems without adequate safeguards, sensitive information—like company or customer data—might be shared. This use can present endless compliance issues and lead to fines for noncompliance with regulations like the General Data Protection Regulation (GDPR). 

• Hallucinations and accuracy concerns: Many generative AI capabilities and their outputs are prone to hallucinations or accuracy errors. If employees are using AI models to improve their decision-making, these hallucinations can lead to incorrect insights, inefficiencies, and faulty workflows. 

While data privacy risks and hallucinations loom large, they don’t have to halt an organization’s AI journey. The key lies in how organizations approach AI adoption—balancing innovation and automation with proper oversight.

Common Organizational Approaches to AI Use

Presented with the benefits and risks of shadow AI, organizations typically take one of three approaches to manage AI use:

• Banning AI use: A ban might make sense for companies in highly regulated industries, but it also risks losing their competitive advantage and frustrating their high-performing employees. 
• AI partnerships: Organizations may partner with a specific, vetted AI vendor, giving employees access to trusted tools (machine learning models, chatbots, and other advancements) while ensuring compliance. AI governance is typically a significant selling point of these partnerships, as vendors are contractually obligated to mitigate internal security risks before the organization or company data is affected.‍
• Guardrails: Instead of banning AI altogether or partnering with a particular vendor, some organizations prefer to provide training and establish guidelines for safe AI usage. Training employees on approved AI use cases is a great way to foster innovation and streamline risk management.

Regardless of the approach you decide to take, understanding where and how shadow AI exists within your organization is essential. Identifying unmonitored AI usage is the first step toward crafting a comprehensive and secure AI governance strategy.

Identifying Shadow AI: A Critical First Step

Organizations attempting to identify and manage shadow AI often face these challenges:

• The proliferation of AI-first tools: New AI-driven tools are emerging daily, each tailored for specific business functions like content generation, customer service, or data analysis. Keeping track of this ever-expanding landscape is a daunting task.
• AI-enhanced existing tools: Widely used platforms, such as Canva or Microsoft Office, are embedding AI features into their core offerings. 
• Blurred work-life boundaries: Employees often adopt AI tools for personal convenience but end up using them for work-related tasks as well. 
• Underground usage: Employees may hesitate to disclose their AI usage without clear guidelines and an open, supportive environment. The fear of repercussions can drive the use of shadow AI tools, where employees continue to adopt new applications without IT oversight or security vetting.

To overcome these challenges and be successful, you’ll likely need to combine employee education with ongoing monitoring.

How to Identify and Mitigate Shadow AI with Guardrails

• Step 1: Audit existing tool usage - Review what software and tools employees are currently using across your organization. Your audit process can include checking for tools with AI capabilities that are already integrated into existing workflows and identifying overlaps between personal and professional AI usage. 
• Step 2: Implement technical monitoring - Start leveraging monitoring tools to detect shadow AI activity. Analyze data from internet gateways and firewalls or review sign-on activity from identity providers to identify unapproved tool usage. You can also deploy specialized surveillance tools to track and manage shadow AI specifically.
• Step 3: Establish clear usage guidelines - Develop and share a comprehensive AI use policy, ensuring it covers what AI tools are approved, the process for requesting and approving new tools, and the responsibilities and expectations for each employee. 
• Step 4: Create a reporting mechanism - Encourage employees to self-report AI tools they’ve adopted by providing assurance of no repercussions and installing incentives for transparency, such as streamlined approval. 
• Step 5: Train employees on AI risks - Host ongoing training sessions to educate existing and new employees on the dangers of shadow AI, the benefits of only using approved tools, and how to identify risky AI applications. 
• Step 6: Monitor and update regularly - Shadow AI tools will continue evolving as more applications implement AI features. Therefore, identifying shadow AI is an ongoing process that requires periodic reviews, audits, policy updates, and new monitoring techniques. 

By following these steps, you can systematically identify, address, and mitigate shadow AI while fostering a culture of trust and collaboration throughout your organization.

UpGuard’s Solution: User Risk

Identifying shadow AI is a critical challenge for security leaders and IT teams. That’s why UpGuard is developing a new product that monitors and manages user related risks including shadow AI.

Interested in learning more? We’d love to chat about how UpGuard can help you stay ahead of the AI curve while managing its associated risks.

To schedule a personalized consultation, visit https://www.upguard.com/contact-sales