Ask any security professional what their biggest headache is with data loss prevention (DLP) solutions, and you can bet they’ll say: false positives. Erroneous noise, which happens when a policy is triggered by mistake, has become all too common.
Plus, every alarm needs to be investigated, meaning these noisy solutions drastically harm productivity in the IT department, creating hours of manually-intensive work and wasting valuable resources.
Not to mention that ‘alert fatigue’ is a real problem. Research shows, when security teams are overwhelmed by alerts, they’re more likely to miss valid security threats.
Of course, getting rid of DLP isn’t an option. From a compliance and cybercrime standpoint, these tools are vital. But not all are considered equal.
In fact, if you’ve got a case of noisy DLP in your hands, chances are you’re relying on regular expressions for pattern recognition. Here’s why that’s an unwise thing to do.
What are regular expressions?
Regular expressions are a form of search tool, relying on characters and symbols tho help security teams discover particular patterns in text data. These patterns could be things like the number of characters, the letters used, or the numbers present. For instance, you could set up a regular expression to find social security or credit card numbers within a bunch of text.
However, here’s the catch: while regular expressions are quite adept at identifying patterns within structured data, they’re not so great at uncovering sensitive information in unstructured formats. So, if the text doesn’t fit perfectly into the predefined patterns outlined by the regular expression, it’s likely to go unnoticed.
But beyond accuracy, the biggest criticism of regular expression-based DLP is the fact it generates overwhelming false alarms. This happens because these tools don’t take into account variations that might appear in a piece of text – for example, mistaking a reference code for a credit card number.
Regular expressions & the cloud
Back in the days of on-premises work, regular expressions did an okay job. But things have changed dramatically with the rise of cloud computing, generative AI and distributed work environments. The sheer volume and speedy exchange of unstructured data have thrown a wrench into the traditional DLP mechanisms.
The truth is, the older DLP solutions just don’t cut it in this new era of data sharing. The massive amount of data flying around and the resulting noise end up making it tough for information security and compliance teams to focus on real risks. With so many false positives, preventing a breach is like trying to find a needle in a haystack.
What used to work well for in-house setups simply doesn’t fly in the realm of Software as a Service (SaaS). SaaS applications are spread out and decentralized, and their data doesn’t play nice with the rules regular expressions impose. Regular expressions have proven to struggle with detecting sensitive elements in this unstructured data environment. The result? Lots of noise and inaccurate alerts, many of which are false positives that need human intervention to sort out.
The alternative: DLP infused with natural language processing
While, a decade ago, security teams had to accept the false positives that came with embracing DLP, today things have changed – and it’s all thanks to natural language processing (NLP).
NLP is a fast-developing subset of artificial intelligence that gives computer systems the ability to understand and analyze the human language in both written and verbal formats. These amazing tools are made up of neural networks that analyze human language, syntax and grammar in real-time and at lightning speed.
Best-in-breed solutions within this arena feature self-learning capabilities, which allow the NLP model to self-develop and improve based on new data, without further input from their creator.
With NLP for pattern recognition, DLP becomes much more reliable and, importantly, quiet. NLP-based systems are fantastically accurate, meaning low-noise, enhanced compliance and better data protection.
Polymer DLP: AI-powered, low-noise protection
Polymer DLP goes beyond RegEx, using NLP algorithms and models to discover, classify and secure sensitive data across your SaaS applications and generative AI tools. Here’s how our tool defies the traditional limits of DLP:
- High fidelity: Polymer DLP is designed to overcome the traditional pitfalls of DLP, offering a low false positive rate and high true positive ratios thanks to the fusion of natural language processing and regular expressions. The dynamic nature of our self-learning algorithms also keeps you ahead of new threats and evolving data trends.
- Autonomous: Gone are the days of locking down applications and shares. Our approach safeguards data without stifling business interactions, autonomously remediating potential instances of data exposure without the need for manual intervention. By avoiding unnecessary quarantines and hurdles, we achieve a harmonious blend of security and operational fluidity.
- Granular permissions: We’ve created Polymer DLP with granular IAM policies, letting you dictate who gains access to sensitive data and who doesn’t. It’s all about nuanced permissions that cater to individuals across diverse SaaS platforms, rather than ill-fitting “off the shelf” controls.
- Quantifiable value: Demonstrating the value of DLP used to be a challenge, but our data exposure risk score changes the game. It’s a metric that quantifies the presence of sensitive data, both inside and outside the organization. This score lends a measurable edge to data loss prevention efforts and allows for an accurate ROI calculation.
See how Polymer can protect your organization
Organizations no longer have to accept that DLP and false positives go hand in hand. With Polymer DLP, security teams can revolutionize how they approach data protection and compliance in the cloud, thanks to the power of NLP.
Ready to get started? Request a demo today.