• DLP software solutions gain profound insights into the lifecycle of every file by processing billions of events and files daily across various platforms, including SaaS applications, public cloud services, websites, and endpoints.
  • Data loss prevention (DLP) software solutions ensure comprehensive protection for the sensitive data you hold across various channels, including clouds, networks, email, endpoints, and users.

Identifying and protecting sensitive data becomes more difficult in the ever-changing field of data management due to the large volume and increasing complexity. Modern communication is characterized by its rapid speed and a shift in user behavior toward sharing images for idea expression, visual proofing, contact details exchange, and other purposes. This shift has strained traditional data loss prevention (DLP) software, primarily reliant on static text-based identification methods.

But what is this, and what makes it crucial today?

What Is Data Loss Prevention (DLP) Software?

DLP software is a security solution that safeguards sensitive data and information against unauthorized access, use, disclosure, and transmission. It categorizes data into regulated, confidential, and business-critical segments, detecting breaches of policies set by organizations or predefined policy packs, often aligned with regulatory compliance standards like HIPAA, PCI-DSS, or GDPR.

Upon identifying violations, DLP initiates remediation through alerts, encryption, and other protective measures, preventing users from unintentionally or maliciously sharing data that could pose organizational risks.

A data loss prevention (DLP) software application oversees and manages endpoint activities, filters data streams on corporate networks, and monitors data in the cloud to safeguard information at rest, in motion, and in use. Additionally, DLP offers reporting for compliance and auditing purposes, pinpointing weaknesses and anomalies for forensic analysis and incident response.

Examining DLP clarifies modern data security best practices and opens doors to comprehending the technological innovations and flexible tactics that have molded the dynamic response to changing data security issues.

How Have Data Security Challenges Evolved?

As data evolves and diversifies, conventional DLP approaches struggle to keep pace. While essential, reliance on text analysis, including thousands of regular pattern detections, content matching, data fingerprinting, and optical character recognition (OCR), falls short of addressing the contemporary data security and privacy landscape. Images, screenshots, and photos complicate sensitive data detection, demanding a more dynamic and sophisticated approach.

How does DLP software provide users with advanced data insights?

DLP software solutions gain profound insights into the lifecycle of every file by processing billions of events and files daily across various platforms, including SaaS applications, public cloud services, websites, and endpoints. This contextual understanding of user activities and corporate data ensures precise detection and classification, minimizing false positives.

Now, we’ll explore the symbiotic relationship between DLP’s sophisticated data insights and the transformative influence of artificial intelligence and machine learning.

What Is The Role of Artificial Intelligence and Machine Learning In Data Loss Prevention (DLP) Software?

Artificial intelligence (AI) and machine learning (ML) are pivotal in reshaping data protection strategies. AI, a broad term encompassing programs mimicking human brain functions, has recently witnessed significant advancements. Machine learning, a subset of AI, involves programs designed to learn from examples and find applications in cybersecurity.

Integrating AI and ML enhances data protection’s overall efficiency and accuracy, reflecting a commitment to staying at the forefront of technological advancements. But why should you consider getting a DLP solution?

Data loss prevention solutions

Data loss prevention (DLP) software solutions ensure comprehensive protection for the sensitive data you hold across various channels, including clouds, networks, email, endpoints, and users. Integrating with the Security Service Edge solution assures and provides unified policy enforcement.

With the help of a DLP solution, you can discover, monitor, and safeguard sensitive data across diverse environments. It offers unified data protection policies and centralized management through a single console. The cloud-based DLP is context-aware, allowing dynamic actions based on granular data risks, such as allowing, blocking, notifying, coaching, quarantining, encrypting, or applying legal holds.

With a foundational understanding of the pivotal role played by AI and ML in data loss prevention (DLP) software, let’s delve into a more detailed examination of how ML enhances and refines DLP strategies.

How Does Machine Learning Improve Data Loss Prevention (DLP) Software?

Machine learning offers a rapid and effective method of identifying sensitive information within unstructured data, particularly in file classification. By complementing traditional DLP rules, ML classifiers accurately categorize documents and images based on similarities. The machine learning models excel at categorizing diverse data types, from tax forms to patent documents, enhancing security and minimizing false positives. Here are the benefits of data loss prevention (DLP) software:

  • Image classification with deep learning

Image classification, a crucial component of DLP, utilizes deep learning and convolutional neural networks (CNNs) to recognize visual characteristics within images. Unlike traditional OCR methods, ML-based image classification provides a more accurate, resource-efficient, and secure alternative. You can also employ transfer learning to fine-tune pre-existing CNNs, ensuring high accuracy and reduced training time.

  • Training data and privacy considerations

Prioritizing customer privacy involves collecting positive and negative sample images from various sources, excluding customer data. Training data includes thousands of actual cloud images, with meticulous labeling of negative examples and adversarial samples. This approach ensures robust classifiers that can distinguish between sensitive and non-sensitive data.

The focus is on image and document classification, utilizing advanced artificial intelligence (AI) and machine learning (ML) technologies to address the evolving challenges diverse data types present.

  • Tailored data augmentation for computer vision

A commitment to accuracy is evident in utilizing a comprehensive suite of synthetic data augmentation techniques tailored for computer vision applications. These techniques, including rotation and cropping, are customized to ensure maximum fidelity with the image data encountered in real cloud environments. Custom augmentations seamlessly integrate documents onto realistic backgrounds, simulating diverse settings for enhanced classifier training.

  • CNN-based image classifiers

The development of Convolutional Neural Network (CNN) — based image classifiers plays a crucial role in accurately identifying images containing sensitive information. These classifiers cover a spectrum from passports and driver’s licenses to credit and debit cards, along with various screenshots. Their seamless integration enables real-time, granular policy controls, enhancing data protection.

  • Document classification with ML

Document classification, a key aspect of data loss prevention (DLP) software, leverages ML to improve accuracy and reduce false positives. In contrast to traditional text-matching or regex-based rules, ML-based document classifiers automatically categorize documents into different types. These may include source code, tax forms, patents, and more. The dynamic learning of patterns in real-time eliminates the need for manual configuration, offering a more adaptive approach.

  • Text classification and NLP

Text classification, a fundamental natural language processing (NLP) model task, involves extracting content from documents. On the other pre-trained language models serve as encoders to convert documents into numeric values, capturing contextual and semantic information. Document classifiers, trained using fully connected neural network layers, accurately identify over 12 types of documents containing sensitive information.

  • Train Your Own Classifiers (TYOC)

Recognizing the diversity of sensitive data across industries, the introduction of Train Your Own Classifiers (TYOC) empowers organizations to train their classifiers for images or documents while maintaining data privacy. Advanced contrastive learning techniques allow organizations to focus on their specific information needs.

In Summary

Integrating AI and ML technologies into data loss prevention (DLP) software solutions reflects a commitment to continuous innovation in data protection. The emphasis on image and document classification provides resource efficiency, higher accuracy, and enhanced security compared to traditional approaches.

The offering is available as a part of various license options, catering to diverse organizational needs. Continuous expansion of AI and ML technologies ensures adaptability to evolving security challenges.

As organizations navigate the complexities of data security, a reliable partner emerges, leveraging state-of-the-art technologies to safeguard sensitive information and adhere to compliance regulations.

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