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Summary

  • Shadow AI usage is a data breach waiting to happen in most organizations.
  • Without the permission of IT, employees are embracing generative AI tools and putting data security and compliance at risk.
  • Banning generative AI will only exacerbate the issue.
  • To shine a light on shadow IT, create a comprehensive usage policy, invest in employee education, and support both endeavors with data loss prevention (DLP) specifically designed for AI applications.

Generative AI is like the Internet of the late 1990s. It’s going to completely reshape the business world, and make employees way more productive, innovative, and efficient. 

In fact, already, Nielsen Norman Group has found that employees using generative AI tools like ChatGPT perform 66% better than those who don’t. 

There’s just one problem. A lot of employees are using generative AI tools without security leaders knowing. And that, unfortunately, is a data breach waiting to happen.

This phenomenon is known as Shadow AI, and it’s set to be one of the most prominent causes of data leaks in 2024 and beyond. 

What is shadow AI?

Shadow AI refers to the usage of generative AI tools, like ChatGPT, Bard and so on, without the permission of a company’s IT department. In practice, this might look like employees using generative AI for tasks like writing content, coming up with artwork or even generative code. 

While all of this sounds harmless enough, these actions pose a considerable challenge for security teams in terms of data governance. 

Without visibility into what data has been shared, and within which applications, it’s impossible for security departments to meet compliance mandates and safeguard sensitive data (AKA: a host of data breaches and huge compliance fines just waiting to happen!)  

The major risks of shadow AI 

The risks of shadow AI fall into three categories: data leakage, data breaches and compliance violations. 

Firstly, it’s crucial to understand that generative AI platforms learn from the data they are provided. This means that any sensitive information inputted by employees could potentially be regurgitated in responses to other users, which would be an immediate data breach. 

Complicating matters, AI tools, like any software, are susceptible to bugs and vulnerabilities. For example, a notable incident in March 2023 involved a bug that inadvertently allowed certain ChatGPT users to access titles from the chat history of others, exposing payment information for several ChatGPT Plus subscribers.

On top of that, we must remember that inputting sensitive data into generative AI introduces organizations to the risk of violating industry standards such as HIPAA, PCI, GLBA, and GDPR. Even seemingly innocent actions, like sharing a prompt containing personally identifiable information, can be deemed a compliance violation, leading to substantial fines. 

What to do to combat shadow AI 

Chances are, shadow AI is already a problem in your organization. You just don’t know it yet. 

While your instinct might be to outright ban generative AI in response, this approach could inadvertently drive employees further into the realm of shadow AI usage.

So, what’s a business to do? Here are three steps to illuminate the shadows of AI:

Create a generative AI usage policy 

Establishing a clear generative AI usage policy acts as guardrails for your employees. We recommend the following categories:

  • Prohibited uses: Strictly forbid activities like using ChatGPT to scrutinize confidential company or client documents or assess sensitive company code.
  • Authorized uses needing approval: Certain applications may be permitted with authorization from a designated authority. For instance, generating code using ChatGPT may be allowed, provided an expert reviews and approves it before implementation.
  • Permitted uses: Allow certain activities without prior authorization, such as using ChatGPT to create administrative internal content like generating ideas for icebreakers for new hires.

Invest in real-time security awareness training 

Crafting policies is one thing, but ensuring employees follow the rules is another challenge. While training can be helpful, traditional one-off training sessions often fall short when it comes to knowledge retention.

Luckily, real-time AI-based employee training can be highly effective. For example, our tool, Polymer data loss prevention (DLP) for AI, leverages AI and automation to provide in-the-moment security training nudges. 

These prompts notify employees of risky dating sharing in-the-moment, blocking actions that put sensitive data at risk. Within one week, our training nudges are proven to reduce repeat offenses of sensitive data sharing by over 40%. 

Protect against the inevitable 

While policies and training are pivotal in minimizing the risk of generative AI data leakage, relying solely on your employees isn’t wise. After all, 91% of data breaches are attributed to insider threats.

To fortify your policies, it’s crucial to deploy the right tools to prevent data leakage and theft in AI platforms. And that’s where our DLP for AI solution comes into play. 

How Polymer DLP for AI can help 

Polymer DLP for AI was designed to empower organizations to reap the rewards of generative AI while combating security and compliance risks. 

Here’s how we support organizations in using apps like ChatGPT and Bard securely: 

  • Granular visibility and monitoring: Using our advanced monitoring system, Polymer DLP for AI is designed to discover and monitor the use of generative AI apps across your entire ecosystem. Harnessing the power of natural language processing (NLP) and automation, our tool provides granular insights on user behavior, data movement and policy violations, all which upholding data security and compliance. 
  • No more data leakage: Polymer DLP seamlessly and accurately discovers, classifies and protects sensitive information bidirectionally with ChatGPT and other generative AI apps, ensuring that sensitive data is always protected from exposure and theft.
  • Real-time education: Our platform supports point-of-violation training, providing real-time nudges to users when violations occur. Additionally, Polymer offers workflows that allow users to accept responsibility for sharing sensitive data externally when it aligns with business needs. 

Ready to illuminate shadow AI usage in your organization? Read our whitepaper to find out more.

Polymer is a human-centric data loss prevention (DLP) platform that holistically reduces the risk of data exposure in your SaaS apps and AI tools. In addition to automatically detecting and remediating violations, Polymer coaches your employees to become better data stewards. Try Polymer for free.

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