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Summary

  • Traditional DLP tools fall short in today’s cloud-centric world, causing frustration with false positives and manual configurations.
  • AI-driven DLP, with NLP, excels at understanding context, classifying unstructured data, and self-refinement.
  • Key features include NLP-based data classification, easy out-of-the-box configuration, user training, and contextual enrichment.
  • These AI enhancements reduce false positives, automate responses to security incidents, and improve overall data security.

Data loss prevention (DLP) tools have long been a cause of frustration in the security operations center (SOC). Known for generating an overwhelming amount of false positives and being manually intensive to configure, many security teams see DLP as more of a burden than an asset. 

But it’s not DLP, in itself, that’s the problem. It’s that outdated, legacy DLP solutions aren’t built for the cloud-first world. These tools are responsible for DLP’s bad name, and the inundation of false positives plaguing the SOC.

Luckily, there’s a reason these tools are called  “legacy.”. With the advent of AI, organizations have the opportunity to embrace a new generation of DLP—one that promises high fidelity, accuracy, and efficiency. 

The trouble with legacy DLP

Legacy DLP, built for regular expressions and structured data, falls short in today’s dynamic, cloud-first landscape. These outdated solutions, relying on simplistic algorithms and dictionaries, necessitate constant manual intervention to adapt to the ever-evolving nature of sensitive data.

While legacy DLP platforms shine in detecting data in standardized, structured formats, they falter in the face of the unpredictability inherent in sensitive data within SaaS environments. This leaves a significant gap, where information circulating in Slack chats, PDFs, or Word documents often slips through the cracks.

More than that, the rigidity and simplicity of these tools mean they usually fail to detect sensitive information while inaccurately flagging harmless data strands. 

Take a debit card number as an example—Legacy DLP doesn’t possess the intelligence to differentiate credit card data from something like a customer reference code or phone number. This results in a barrage of inaccurate alerts that undermine the security team’s ability to respond to genuine threats.  

AI-enhanced DLP 

AI is the direct solution to the limitations of outdated DLP systems. Enhanced with natural language processing (NLP), AI-driven DLP solutions excel in grasping context, identifying data in unstructured formats, and refining themselves without manual intervention. Combined, these capabilities create a DLP solution with unparalleled accuracy and precision. 

Here’s a deeper look at the role AI can play in enhancing accuracy and reducing false positives throughout the DLP lifecycle.  

NLP-based data classification 

NLP is a branch of AI that understands the intricacies of human language. It excels at discovering and detecting unstructured sensitive data within documents, images, web chats, etc. 

To that end, organizations can harness the power of NLP to detect and classify sensitive data in unstructured formats autonomously. In particular, look for NLP-infused tools that extend data protection to cloud applications like Slack, Microsoft Teams, and ChatGPT, where most enterprise communications happen today. 

Flexible remediation   

Best-in-class DLP solutions can be configured to work out-of-the-box. They align with compliance templates like HIPAA or GDPR and reduce the need for manual intervention once an event is detected. 

These tools scan digital environments in real time to discover and prohibit incidents of improper data sharing and compliance violations. Depending on the severity of the incident, an advanced DLP tool will automatically take remediation actions such as: redacting or deleting sensitive data, blocking the action in question, or alerting the security team if the situation warrants further investigation. 

User training 

Detection alone is not sufficient; behavior change is essential. AI-enhanced DLP tools can facilitate a culture of security by incorporating user training at the point of violation. 

When a policy is violated, these tools not only address the issue through the appropriate security control but also educate the user, making them less likely to make the same error in the future. 

Contextual enrichment 

One of AI’s most powerful facets is its ability to learn independently. Like a human brain, AI neural networks expand and enhance from every interaction. The longer you use an AI-powered DLP tool, the more precise it will become. It will gain contextual knowledge of your organization to refine security policies, understand user behavior, and better uphold data security. 

When searching for a DLP solution that will scale with your organization’s security needs, especially if your employees rely on highly-collaborative cloud apps, consider one that leverages AI. Features like NLP-based data classification, flexible remediation, user training, and contextual enrichment will reduce false positives and can automatically improve your security posture.

Polymer is a human-centric data loss prevention (DLP) platform that holistically reduces the risk of data exposure in your SaaS apps and AI tools. In addition to automatically detecting and remediating violations, Polymer coaches your employees to become better data stewards. Try Polymer for free.

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