Dark data, or data that you’re either unaware of or simply not using, is both a lost opportunity and a security risk. Even shadow data, where you may be aware it exists but it holds no active purpose, is a threat to your company.
Data is currently the name of the game. AI companies are trying to harvest every byte of data available online today, while everyone else is trying to use AI systems to analyze every file they have for greater insights and automation.
With so much data out there, it’s imperative that you get a handle on it. Not only can you your business operate better, but you can secure yourself against the emerging threats in 2026 and beyond.
How Much Data are We Talking About?
On average, 64% of organizations have at least 1 petabyte of data at their fingertips. 41% have over 500 petabytes of data to manage at any given time. That’s a whole lot of data. Having that much data isn’t a bad thing, it’s just that, on average, experts estimate that between 40 and 90% of it remains in the dark. This means that the data in question is unorganized and unused. It’s a sitting duck waiting to be exploited, either by you or hackers.
How to Protect Your Business Against Dark Data
There are several ways that you can start protecting your business against dark data and the risks it carries.
· Start with Your Cloud Security
Chances are that your data is either entirely stored on the cloud or at least portions of it are in the various SaaS solutions you’ve implemented. Not only is this cloud data at risk of getting lost in the noise, but you also have to contend with the fact that the server your data is on will also be shared with multiple other clients. To understand why this is an issue, read through this Red Canary guide on SaaS security risks and discover how to better protect your cloud-based data.
· Structure Your Data
Dark data is either untapped potential or a security risk. To avoid this, it’s imperative that you start structuring it. The good news is that you no longer need to spend hours doing this manually. Thanks to natural language processing (NLP), natural language understanding (NLU) and even speech recognition, AI systems can now understand and therefore analyze and even sort this dark data better than ever before.
That doesn’t mean you can simply sit back and let your data be sorted. AI systems frequently make mistakes, which means it would benefit significantly from improving data governance rules for any incoming data. Being able to better mark up and catalog the data you collect and generate will mean that AI analytics can work faster and better, as it contains the context as well as the raw data itself.
· Silo Your Data
The last step that can help you find shadow data, delete any duplicates, and finally remove superfluous datasets is to funnel your data into a single data warehouse. This warehouse will act as a single source of truth. It’s where all your systems get their information from, meaning there will be no inconsistencies, no shadow data, and no weak points.
