Data discovery can be a delicate, difficult task that dominates a day. Any task like that can be time intensive, and with any data discovery product or process, you will inevitably run into the ‘false positive problem’.
The false positive problem is when data is identified that looks exactly like the type of data you’re trying to locate, but isn’t. This can add time to your discovery process, and complicate it. The big problem here is that time will need to be spent trawling through vast amounts of data to pick out what is or isn’t the data you’re looking for, and that’s time that could be better spent doing other, more productive things.
Thankfully, the false positive problem is an area QuasarScan is engineered to eradicate. Instead of spending money on a tool that runs an analysis and leaves you with tedious, time-consuming manual work to do on the other end, is it not better to trust a platform that prides itself on finding credit card data while weeding out bad results?
As a veteran of this industry, QuasarScan has come up with a few ways to address the issue. So buckle up and stay tuned as we explore them in the article below.
Score, then sort
To address false positives, the first thing you need to do is accurately score data based on how likely it is to be a card. QuasarScan’s system presents you with the data you’re most likely to be looking for, before logically working its way through to the data least likely to be what you’re looking for.
By laying data out in a graded, easy-to-sort manner, the system lets you work quickly and effectively analyse huge amounts of information before making real-time decisions based on the risk profile of your organisation. For example, a large bank may sort through all the ‘likely’ ‘maybe’ or ‘unlikely’ data hits, whereas a small travel company may only choose to clean up the high risk data.
What do we mean by maybe or unlikely data hits? Well, at first glance a phone number or a bank account could look like a credit card number – but under QuasarScan’s weighted scoring system, you’ll easily see what’s a card and what isn’t.
Once the data has been scored, it’s a quick process to go through it using QuasarScan’s tool and then export it. The beauty of sorting the data in the platform is that analysts will get a clean data score and won’t have to spend valuable time fixing the report before proceeding with any action.
Another benefit to QuasarScan’s product is that it allows analysts to look for common root causes of a problem and then fix it. When deciding what to do, fixing the root cause is going to provide a greater benefit than just deleting data since this means you can stop the problem at the source and save time during any subsequent scans.
Present data in a way that leads to targeted action
We touched on this briefly in the section above, and once you’ve properly scored, analysed and condensed your data into what you’re looking for, you can present it in a way that leads to clear and targeted action.
What does this action look like? Well, it could be using the clean and verified evidence you now have to support things like improving internal processes, ensuring your staff are skilled at handling confidential data securely, and fully understand the importance of doing so.
Finally, QuasarScan then re-scans the targets to ensure all data is ‘clean’, and continues scanning to make sure the problem doesn’t pop up again. But if it does, it’ll be caught early and sorted quickly.
Conclusion
The false positive problem is a large topic that can be covered in far greater detail than we have here, but hopefully these few tips have helped you understand it, and the methods used to address it.
QuasarScan has spent 10 years developing and fine-tuning the proprietary weighted scoring system that puts you in control of your scan. Why’s that important? Well, it enables you to make accurate, data-driven decisions that specifically serve your priorities and objectives.
