Shadow AI Risks: What Are They?

The widespread adoption of artificial intelligence (AI) tools and technologies can provide companies with a bouquet of exciting benefits, like improved efficiency, data analysis, and decision-making. However, AI adoption is only effective when it’s done in a strategic, responsible, and secure manner. When AI is brought into enterprise environments unofficially, it can open the floodgates to many security risks.

In this article, we’ll zero in on shadow AI, a growing phenomenon that’s causing headaches for numerous enterprises. We’ll break down what shadow AI is, highlight its many dangers, and provide some actionable advice on how to mitigate potential risks. 

What is Shadow AI? 

When we say shadow AI, we’re talking about any AI tool or service that anyone from an organization commissions or uses without the approval or oversight of official IT and security teams. It’s basically the AI version of shadow IT, which refers to any IT service that falls outside the visibility and management of official IT and security teams. 

So, what examples of AI applications fit the definition of "shadow AI"? Technically, any AI technology that official IT and security teams don’t know about is shadow AI. This includes data visualization tools, simple AI-powered grammar checkers and writing tools, AI models for data analytics, and generative AI (GenAI) tools based on large language models (LLMs) like OpenAI’s ChatGPT.

Shadow AI is particularly hard to spot because it isn’t often a result of malicious activity. It’s typically just a case of a busy employee using a few external AI solutions to make some tasks easier. Unfortunately, those innocent actions have significant risks, and these risks can lead to data breaches and other incidents that violate data protection regulations. 

These days, because of the speed of modern technologies and the cloud, it’s so easy for employees to access and use software-as-a-service (SaaS) solutions. A couple of clicks and a few minutes are all it takes to introduce shadow AI tools and all manner of unprecedented security vulnerabilities. But what exactly are those security risks? 

What are the Risks of Shadow AI?

The unofficial use of AI is a major cybersecurity vulnerability, and the security risks associated with unauthorized AI tools can snowball into major incidents. In this section, we’ll break down the biggest risks of using AI systems that IT teams don’t know about. 

Data Privacy and Security Compromises 

Employing AI systems without official oversight introduces serious data security risks. The inputs and prompts that employees enter into AI tools are often stored by those tools for self-optimization. Without proper awareness, workers could inadvertently feed confidential content and sensitive information into external AI systems.

When sensitive customer or company data isn’t under the control of the enterprises, there’s no telling what could happen. Data breaches, regulatory violations, legal repercussions, and reputational damage are all very real possibilities.  

Lack of Transparency and Accountability 

Consider a situation where multiple employees use different unauthorized AI or machine learning (ML) tools for a project. If there are issues down the line with data or insights gathered with the help of those tools, businesses will struggle to track the root cause of the problem. 

Therefore, if there are no AI governance measures in place and if employees are onboarding new tools without the knowledge of the IT department, it could potentially create a blame-heavy culture with little ownership of decisions.

Non-Compliance Incidents

Almost every industry or geography has a stringent set of compliance obligations that businesses have to fulfil. When businesses are riddled with shadow AI, there’s always the risk of data privacy and security slip-ups. The smallest compromise of customer data or business secrets can snowball into a long list of compliance issues. 

Businesses that have to comply with regulations like HIPAA, CCPA, GDPR, and PCI DSS must be extremely careful with shadow AI because the fines could be severe. While large multinational corporations might survive such fines, small and medium businesses can struggle to recover from the regulatory repercussions of shadow AI.

Reputational Harm

Today, most businesses are judged by how well they unlock and leverage new technologies like AI. As a result, if a company faces issues due to shadow AI, it could lead to lasting damage to its reputation that may be difficult to recover from. 

Furthermore, if businesses leverage shadow AI tools that are biased or suboptimal, it could result in outputs and decision-making that don’t align with the overarching ethics and quality standards of the organization. 

How to Mitigate Shadow AI Risks? 

The following are some critical best practices and recommendations that can help companies tackle the omnipresent risk of shadow AI.

Establish Clear Guidelines for AI Use

Businesses should craft and enforce strong AI policies and guidelines when it comes to the adoption of AI tools. Creating an allowlist of AI tools that employees can use is a good way to start. It’s also crucial to create simple workflows for employees to gain access to authorized tools. 

Always remember: It’s not about keeping employees away from AI capabilities; it’s about ensuring responsible AI adoption.  

Regularly Update Your AI Allowlist

An allowlist and a blocklist for AI tools is a smart way to keep your employees away from unauthorized AI apps. However, keep in mind that an AI tool that’s considered safe today might not be safe in a few months. Therefore, businesses have to keep their eye on emerging shadow AI risks and adapt their allowlists and blocklists accordingly. 

Use AI Risk Management Frameworks

As businesses keep applying AI across diverse use cases, new risks will emerge, both in plain sight and in the shadows. AI risk management frameworks can be useful in understanding and mitigating the many risks associated with AI, including shadow AI.   

Unsure about what AI risk management frameworks to use? Take a look at these: 

• MITRE’s Sensible Regulatory Framework for AI Security
• ISO/IEC 23894:2023
• NIST AI RMF

Also, businesses have the option to pick and choose the most relevant aspects of these frameworks and build their own AI governance framework. 

Tighten Access Controls 

As mentioned earlier, one of the biggest risks of shadow AI is when employees accidentally share sensitive data with unauthorized apps. A quick way to reduce the possibility of that happening is to curtail access to crown jewel data. 

Zero trust principles like least privilege can help businesses secure their most sensitive data. Basically, with least privilege and multi-factor authentication, companies can make sure that access privileges to sensitive data are granted only when absolutely essential. 

Prioritize AI Awareness and Education 

Getting rid of shadow AI should never involve friction between a business and its employees. Businesses should focus on training employees on responsible AI use and educating them on the threats posed by shadow AI. 

Through this process, it’s important to have an open dialogue with employees to understand why they feel the need to procure unauthorized AI tools. This can help organizations weave new authorized AI tools into their allowlists and tech stack. 

Keep in mind: Mitigating the risks of shadow AI should be a collective effort, involving everyone from the CIO and CISO to the junior-most employees.

Use Real-Time Threat Monitoring Tools 

Shadow AI risks can quickly mature into incidents. While mitigation strategies can minimize the possibility and risks of shadow AI, there’s always the possibility of a couple of rogue AI resources. By implementing 24/7 threat monitoring tools, businesses can immediately identify anomalies associated with shadow IT risks and swiftly remediate them. 

Conduct Regular AI Security Audits 

From chatbots to data analytics tools, AI is used by almost every department of every modern organization. Some of these tools may be authorized, and others might be examples of shadow AI. 

To ensure a strong and secure AI posture, businesses should perform periodic assessments of how their teams are using AI technologies. This will give businesses insights into how and why shadow AI risks emerge and what steps can be taken to prevent them.  

Monitor the AI Regulatory Landscape

In addition to familiar compliance standards like GDPR and HIPAA, businesses should pay close attention to AI-specific compliance obligations. Legislation like the EU AI Act and state-specific regulations in the US are coming into play, and shadow AI risks can mature into severe compliance disasters if organizations aren’t careful. 

Work with an MSSP with AI Security Capabilities 

The risks of shadow AI can be complex to understand and address. The good news is that businesses don’t have to be alone in this mission to eradicate shadow AI. Working with managed security service providers (MSSPs) is a simple way to hand off your shadow AI concerns to AI security experts. 

With the right MSSP, you can streamline AI adoption, implement cutting-edge AI safeguards, and remove the risks of shadow AI across your organization.

Conclusion

With each passing month, shadow AI tools pose an increasing risk to organizations. While their use is rarely malicious—often driven by a desire for greater efficiency, accuracy, and productivity—the consequences shouldn't be underestimated. Unauthorized AI use can result in security breaches, compliance violations, reputational damage, and a lack of transparency. Understanding these potential risks paves the way for responsible AI deployment.

To mitigate these risks, businesses should introduce guidelines for AI use, have AI allowlists, use risk management frameworks, and tighten access to sensitive data. They should also conduct shadow AI awareness programs, implement threat monitoring tools, conduct regular AI security audits, and keep an eye on how AI compliance requirements evolve. 

Lastly, working with an MSSP is an effective way to round out your shadow AI mitigation strategy.