Improving DLP Accuracy: Reducing False Positives

The Challenge with DLP False Positives

DLP systems aim to protect sensitive data by monitoring, detecting, and preventing unauthorized data transfers. However, legacy systems often suffer from high false positive rates, flagging legitimate actions as potential data leaks. These false positives disrupt workflows, frustrate users, erode trust, and divert resources from addressing actual threats. Despite advancements in detection, false positives remain a persistent challenge due to complex data patterns, rigid rules, and ambiguous contexts.

Strac’s Breakthrough Solution: A Guaranteed Fix for False Positives

Strac’s DLP solutions take false positive reduction to a new level. We have implemented a feedback-driven learning mechanism that guarantees results:

1. Thumbs Down Feedback Loop: When a user identifies an event as a false positive, they simply flag it using the “thumbs down” mechanism.
   • This feedback is immediately logged and analyzed by Strac’s advanced algorithms.
   • The system learns from the specific context—including data patterns, user behavior, and intent—to prevent similar false positives in the future.
2. Contextual Understanding: Our solution employs contextual analysis and machine learning to recognize legitimate business actions. It uses nuanced insights to differentiate between genuine risks and harmless activities.
3. Persistent Adaptation: Strac’s DLP continuously adapts its policies and detection algorithms based on user feedback, ensuring that false positives are resolved permanently without manual intervention.

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Why This Matters:

• Improved Accuracy: Once flagged, similar legitimate actions will no longer disrupt workflows.
• Enhanced Productivity: Security teams spend less time managing false positives and more time addressing real threats.
• Trust Building: Users regain confidence in the DLP system, ensuring adherence to security protocols without frustration.

Real-World Examples

Consider these scenarios and how Strac eliminates the false positive problem:

• Financial Institutions: Emails containing reference numbers are often flagged as credit card numbers. Once flagged as a false positive, Strac ensures future instances of such emails pass through without triggering alarms.
• Healthcare Providers: Routine patient communications with sensitive terminology like “diagnosis” or “prescription” often get flagged. Strac’s system learns this context and avoids repeating the mistake.
• Collaborative Workflows: Teams sharing internal documents marked with terms like “proprietary” are no longer disrupted after the system learns they are within approved sharing contexts.

Strac’s Broader Capabilities

In addition to tackling false positives, Strac offers a comprehensive suite of features to protect sensitive data across SaaS, endpoints, and Gen AI platforms:

1. Discover and Classify: Strac accurately identifies sensitive data, from PII and PHI to financial records and trade secrets, across unstructured and structured environments.
2. Remediate Effectively: Strac enables redaction, blocking, alerting, and secure storage in its vault.
3. Integrations: Native support for platforms like O365, Google Workspace, Slack, Salesforce, and more, with APIs for seamless custom integrations.
4. Compliance Support: Achieve compliance with regulations such as HIPAA, PCI, SOC 2, and GDPR through Strac’s robust solutions.

Why Strac is Different

Strac doesn’t just reduce false positives; we solve the problem. Our innovative technology ensures that every false positive reported is a learning opportunity for the system, resulting in permanent improvements. This proactive approach, combined with advanced data classification and contextual analysis, makes Strac the most reliable DLP solution on the market.

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