IBM’s study of data breaches over the course of March 2023 and February 2024 has revealed critical breach trends that businesses simply cannot ignore, and one of them is the danger of shadow data. 

Shadow data is data that your business has lost track of. This may seem like something that you’re unlikely to do, but it’s easier than you think. Every time you add a cloud account or use an SaaS account, you’re diversifying your datasets. Increasingly the data you use is fractured rather than unified. This makes it harder to keep an eye (or control) over your data. The challenge with shadow data is simple: you aren’t aware it’s even there. 

The Risks of Shadow Data 

Shadow data was found to be a component in around 35% of data breaches in 2023/2024. Shadow AI is another pressing concern. With generative AI tools becoming ubiquitous around society, it’s only natural that they, too, become used in workflows. This happens even without authorization. In short, increasingly, employees are using genAI tools to help with their workflows. This is all well and good until they inadvertently input sensitive or private data into tools like ChatGPT. 

Between shadow data posing as weak points in your data security network and employees inputting your data in places where your sensitive information will then be used to train third-party companies, addressing these “out-of-sight” risks is more important than ever. 

Thankfully, there are things that you can do, starting with following these top tips.

How to Get Rid of Shadow Data and Better Protect Your System

Start By Finding and Securing All Your Data (Including the Shadow Data) 

You can’t protect what you don’t know exists. That’s why the first step is to find, categorize, and secure every file you’ve created. Manually, this would be a mammoth task, or even potentially impossible. Thankfully, there are data security tools that can streamline this process without an agent. This means you can sit back and relax while tools like data security posture management systematically go through your cloud and on-prem dataspheres. Using AI and ML, it then works to understand each file and classifies it so you can easily find files by sensitivity. 

This is just step one. These tools can also check user access rules and make recommendations based on the user’s or tool’s role. It can also flag duplicate files and perform other clean-up tasks that work to secure your files across your network. 

Improve Your Data Governance Framework 

You don’t need all the data that you have. Use the results of the DSPM to start purging redundant, outdated or trivial (ROT) data from your systems. You’ll also want to create an improved data governance framework to prevent future ROT files from accumulating. The less ROT, the easier it will be to keep track of your data. This, in turn, makes it harder for shadow data to pile up. 

Implement Zero-Trust Policies 

Zero trust policies work with the approach that no user or tool has automatic access to any piece of data. Instead, the user will need to meet or pass several criteria before being allowed access. For example, with zero-trust policies implemented, an employee would need additional approvals before working from a third-party location. This will prevent hackers who have gained login credentials through a data breach from accessing the account because their IP address does not match. 

By implementing zero-trust policies alongside behavior monitoring and identity access management, you can lock down data and prevent wide-scale breaches.