Why Traditional DLP Fails to Protect Your Most Sensitive Data

Dec 06, 2023
4 minutes

Safeguarding your business's unique data is more crucial than ever. Traditional Data Loss Prevention (DLP) systems are struggling to keep up. Here’s a clear look at why they don’t suffice and how a “shift-left” approach can benefit your company.

The Gap in Traditional DLP

Most DLP systems are designed to protect standard data like personal and payment information because laws require it. But what about the data that's the heart of your business, like your trade secrets or unique processes?

Legacy techniques strictly follow a predefined set of rules, meaning data unique to your organization can slip through the cracks. A robust DLP solution needs to offer both out-of-the-box machine learning (ML) detection models and customer-trainable ML detection models to provide reliable, accurate, and efficient data loss prevention.

Prevention Before the Problem

What if you could fix a leak before it became a flood?

That’s the advantage of “shifting left” in data security. By constantly monitoring where your data lives and how it's used, you can spot risks earlier and avoid breaches before they happen. This approach is not just about avoiding loss; it's about reinforcing trust in your business’s ability to protect its assets.

Taking a “Shift-Left” Approach to Data Security

At Palo Alto Networks, we understand that one size does not fit all, especially when it comes to protecting sensitive data. Traditional DLP systems have their place, but when it comes to the unique and proprietary information that sets your business apart, you need clarity and cutting-edge capabilities.


That’s where our “shift left” philosophy comes into play, focusing more on recognizing and addressing data security issues earlier in the data lifecycle rather than reacting to incidents after they occur. Here’s how our Enterprise DLP puts this philosophy into action for your business:

  1. Customer-trainable ML detection models: Recognizing the importance of your “crown jewels”—the proprietary and confidential data that gives your business its competitive edge—our approach allows you to train your own models based on your unique intellectual property. Our technology is the only reliable way to identify and take action to protect your sensitive data accurately. By training custom ML models with your specific data, our solution becomes finely tuned to recognize and protect the nuances of your unique data ecosystem.
  2. Out-of-the-box ML detection models: Our approach leverages over 100 predefined document-type detectors for a strong baseline of protection. With the latest advancements in Large Language Model (LLM) technologies, our DLP classifiers now offer unprecedented accuracy and sophistication in identifying and safeguarding business-critical information.
  3. Proactive discovery and protection: Our continuous monitoring extends beyond simply watching over the data; it's about thoroughly understanding where your data is most vulnerable and implementing preemptive defenses. This proactive measure of discovering data at risk due to configuration oversights or inadvertent employee actions means we can provide guidance on strengthening your data security posture before any data loss occurs.
  4. Unified Data Risk Explorer: The Unified Data Risk Explorer is the foundation of our “shift left” approach, giving you a comprehensive overview of potential risks. This powerful tool allows you to delve into the specifics of your data security, sorting by location, user, application, and other risk vectors. It also assesses the likelihood and potential impact of a data breach and provides all this information at your fingertips to help you make informed decisions and proactively secure your data.

Control sensitive data with our proactive “shift left” data security strategy. For a deeper understanding of how our solutions can safeguard your business's most valuable assets, check out our on-demand session from SASE Converge ‘23, “Cover Your SaaS With Next-Gen CASB and AI-Powered DLP.”


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