Key to Solving Data Security’s Trust Issues

In the world of cybersecurity, one of the most ironic dilemmas we face is this: while we preach the virtues of Zero Trust—ensuring that no device, location, or application is automatically trusted—our trust in data itself often leaves much to be desired. I’ve worked for Data Security vendors now for over 15 years. Historically, data security programs have been plagued by one persistent problem: the accuracy of data classification, particularly as it relates to its sensitivity or risk to the business. We can all look for a SSN number, but that isn’t the entire story, a business is full of various business units, creating content of varying levels and types of risk to an organization. The struggle to get data classification right has been a thorn in the side of many IT and security teams. But finally, AI-driven data classification might just be the hero we’ve all been waiting for.

The Pitfalls of Human-Centered Data Classification

Let’s face it: relying on humans to classify data accurately has always been a bit like asking cats to organize your sock drawer. Business units, understandably focused on their primary tasks, often see data classification as a distraction. This can lead to under-classification ("It’s just a spreadsheet, how risky can it be?") or over-classification ("Better mark this as ‘Top Secret’ just to be safe!"), neither of which is helpful. Let's be honest no one in my household can decide which is the most valuable streaming service to pay for!

Then there’s the data governance team. While they’re great at setting policies, they’re not always clued into the day-to-day nuances of business operations. This can lead to a disconnect where the governance team’s classifications don’t quite match the reality on the ground. It’s like trying to play a game of chess when one side thinks they’re playing checkers.

The result? Inconsistent, inaccurate data classification that leads to security blind spots, unnecessary complexity, and, ultimately, risk.

Enter AI-Driven Data Classification

The game-changer in modern data classification lies in the integration of Large Language Models (LLMs) and Natural Language Processing (NLP). These technologies enable a level of context-based understanding that was previously unattainable, driving accuracy rates up to 95%. But how exactly do they work their magic?

Imagine you come across the word “Jordan” in a document. On its own, it’s ambiguous. Is it referring to a person (Michael Jordan, perhaps?), a country (Jordan in the Middle East), or a brand (the iconic Nike Air Jordans)? The traditional data classification systems would struggle here, often defaulting to a generic or incorrect classification based on limited context. The results could be misleading, with sensitive data either under-classified (leaving it vulnerable) or over-classified (leading to unnecessary restrictions).

This is where LLMs and NLP step in. These technologies analyze the words surrounding “Jordan” and understand their relationships and meanings within the specific context of your business. For example:

• If the document mentions "NBA," "basketball," or "Chicago Bulls," the LLM and NLP system can accurately infer that “Jordan” refers to the person, Michael Jordan.
• If the text includes words like “Amman,” “Middle East,” or “travel,” it recognizes that “Jordan” is being used as a geographic reference.
• If the context revolves around “Nike,” “sneakers,” or “fashion,” it identifies “Jordan” as the brand.

But it doesn’t stop there. Beyond just identifying what “Jordan” represents, LLMs and NLP also evaluate the importance of this information to your organization. Is the reference to Michael Jordan part of a celebrity endorsement deal, making it high-value data? Is the mention of the country Jordan part of a sensitive geopolitical analysis? Is the brand Jordan part of a new product launch, crucial to your marketing strategy?

By analyzing not just the keywords, but their proximity to each other and their usage within the context, these AI-driven systems can make incredibly nuanced decisions. This allows for precise classification that accurately reflects the data’s sensitivity and importance to your business.

This ability to comprehend and classify data contextually is a fundamental shift. Where humans or traditional systems may see ambiguity, AI sees clarity. This leads to more consistent, reliable, and efficient data classification that aligns perfectly with the specific needs and risks of your organization.

Zero Trust, Meet Zero Guesswork

One of the most exciting aspects of AI-driven data classification is the speed and scale at which it operates. Imagine being able to enroll cloud applications and their data in just five minutes each. For on-premise services, the process takes a mere hour. This isn’t just a game-changer—it’s a revolution in how we approach data security.

One customer using Cyera was able to discover and classify 100 Petabytes of Sensitive data across its entire network of on-premise and cloud based data stores in just 14 days - saving them 2 years of manual work if they had used legacy tools.

This customer was able to reduce their attack surface by 44 Petabytes - which represented a 70% reduction of overall data risk. Precision and Scale are game changers to the Data Security world that suddenly become possible with sophisticated LLMs and NLPs.

Suddenly, visibility into every piece of data your organization is responsible for is within reach. And with this newfound visibility comes an incredible level of trust—trust that your data is properly classified, properly protected, and properly managed.

Ready for a Zero Trust World?

So, if we can finally trust the accuracy and precision of the data in our environments, and inside of our risk models, data loss prevention environments, are we finally ready for a Zero Trust world? Thanks to AI-driven data classification, the answer might just be “yes.” By solving the age-old problem of data classification accuracy, we’re not just securing data—we’re securing the future of our businesses. With Zero Trust at our core and AI at our side, we can confidently say that the future of data security is bright, precise, and trustworthy.

In the end, maybe Zero Trust isn’t just about not trusting anyone or anything—it’s about finally being able to trust the things that matter most.

If you'd like to learn more get in touch with me or go to cyera.io