The Problem with Data Loss Prevention Methods

The Problem with Data Loss Prevention Methods

There are two main ways to accomplish data loss prevention. These methods aim to reduce the risk of data breaches by limiting exposure to sensitive data like emails, credit card numbers, and patient data.

Rule-Based Policies: A Never-Ending Battle

The first method involves creating numerous rules and policies to prevent unauthorized access to sensitive data. In this approach, you're dealing with a plethora of data structures across numerous cloud services. Security teams expend hundreds of hours crafting policies in multiple languages. However, this is not effective. By the time you've covered 80% of your data, new products launch, and you're back to square one.

Data Lakes: A Delayed Solution

The second method focuses on server-side data loss prevention through data lakes. Data is extracted from various sources, sanitized of personally identifiable information (PII), and then stored in a data lake. The drawback is that most data lakes don't provide real-time data, which is crucial for operational and engineering teams.

The Flaws of Client-Side Data Loss Prevention

Another approach is client-side data loss prevention. This involves installing an agent on user devices that redacts PII. This method, however, complicates the problem by increasing the number of endpoints that need to be managed. It also either redacts too much, affecting work, or is too lenient, risking data breaches.

A New Approach to Data Loss Prevention

Solving the Problem at Its Source introduces a whole new way of handling data loss prevention—solving the issue at the source. We use a Layer 7 proxy that filters out PII before it leaves the original source, whether it's a database or server. This approach prevents the problem from becoming worse.

AI-Powered Data Redaction

We employ artificial intelligence to catalog PII, thereby automating the redaction process. The AI model can identify sensitive data on the fly and redact it before it leaves the source.

Summary offers data loss prevention at the source using artificial intelligence. This keeps the problem manageable and automated, allowing you to focus on what matters. Forget the traditional approaches of rule-based policies and client-side agents. With, you eliminate the delays and inefficiencies.