The Risks of Shadow AI, and How To Use AI Safely Instead

When a global consumer electronics company had a data breach in 2023, it wasn’t just a PR problem — it was a data disaster. Employees had been using a public SaaS-based AI application to write their code. Among the data they put into the AI platform was the source code for some of the company’s proprietary software. When other people used ChatGPT, they were able to find the source code and spoil company confidential trade secrets. 

This was one of the first public instances of shadow AI. Here’s a rundown on what shadow AI is, what the risks are and how to mitigate those risks with private AI.  

What Is Shadow AI?

To understand shadow AI, you first have to understand shadow IT. Shadow IT is the use of any devices, software or services that are outside of the company’s established IT controls and policies. Any tools you are using that aren’t within your IT team’s governance aren’t covered by the same protections and can lead to data breaches. Some common shadow IT tools that employees often use are Dropbox, Google Drive and third party SaaS AI applications. 

When employees use AI applications that aren’t covered by internal IT governance protocols, it’s considered shadow AI. Often, employees mean well — they’re simply trying to finish their work faster or get some fresh ideas. However, if that AI isn’t covered by internal security and governance protocols, then users could potentially be exposing company data to the entire internet. 

Risks of Shadow AI 

Shadow AI has many risks that can affect the user, the whole company and potentially customers as well. 

Security. Without proper IT governance, SaaS AI applications can be a significant security threat. The shadow AI applications employees are using may not have security protocols, which could result in personally identifiable information (PII) or sensitive corporate data leaking to the public domain.  

Inaccuracy. Relying on AI tools that have not been properly vetted by the company means risking inaccuracies from GenAI hallucinations. Generative AI makes decisions based on the information it has access to, so it can also be heavily biased — or completely wrong —  if its input data is limited, not representative or in error. If a user doesn’t double check all the information with reputable sources, the material proposed by AI can be misleading or otherwise inaccurate. 

Compliance violations. Especially when dealing with patient data, financial data or other sensitive information, shadow AI can put your company in serious violation of regulatory and compliance requirements. Data used in shadow AI may not be protected or secure, so sensitive data can be mishandled if it’s used in unapproved AI applications.

Managing the Risks of Shadow AI

Managing the risks of shadow AI within your enterprise can be tricky. The first step is to approach the conversations around AI in general with positivity. AI can be a useful tool to drive innovation — when used properly. In fact, if companies aren’t using AI in some capacity, they’re already falling behind competitors. It’s best to frame the conversation by focusing on how to use AI in a safe and effective way. 

The next step is to find AI tools that you can bring under your IT team’s governance. Some companies can build their own large language model (LLM), but most organizations will need a secure, private and enterprise-grade platform on which they can deploy and run any generative AI workloads — whether they build custom AI apps or use pre-existing applications

Once you’ve identified your appropriate AI tools, it’s time to build out clear expectations. Create a governance framework and develop clear company policies around the use of AI. Work with your internal communications and change management teams to build out a plan to provide training on these policies and educate team members on potential security risks. 

Private AI: How To Use AI in a Safer, More Accurate Way

Because SaaS-based AI applications can have many risks, many companies are opting for private AI. Private AI platforms like SUSE AI provide guardrails and governance over your most sensitive data, keeping bad actors from coming in and your data from going out. Employees can still take advantage of time-saving AI tools, but the company has guardrails in place, ensuring data sovereignty.  

The strict data protections in private AI offer companies peace of mind. Because companies have control over the platform, they can enforce governance policies. Their data is aligned with compliance and regulatory requirements and is safer from data breaches. Additionally, private AI offers business benefits. Companies can customize the algorithms in their private AI to better suit their business needs and complete data analysis and other projects faster. Companies can also tailor the private AI to scale with growth and accommodate additional needs. 

Private AI: The Future of AI at Your Organization 

Don’t let shadow AI steal your company secrets. Learn more about private AI by downloading our whitepaper “How to Deliver AI Safely & Securely — Without Compromising Your Data.”