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Updated: Oct 30, 2023   |   Fergal Glynn

4 ways machine learning improves data loss prevention

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4 ways machine learning improves data loss prevention

Data loss prevention (DLP) solutions are essential components of an organization’s overall cybersecurity strategy. DLP tools are designed to enforce a company’s data handling policy regarding its sensitive and high-risk data. These solutions identify attempts to use or access sensitive data and take the necessary action to ensure it is handled safely. 

DLP tools are responsible for remediating risk by performing actions such as automatically encrypting data before it’s transmitted or restricting unauthorized users from printing sensitive information. Modern DLP platforms employ advanced artificial intelligence (AI) and machine learning (ML) techniques to improve their functionality and performance. Through the use of these technologies, data loss prevention tools can continuously get better at protecting an organization’s valuable data resources.

 

What is machine learning?

Machine learning is a branch of artificial intelligence that enables software tools to become more accurate at making predictions over time but without additional programming. ML algorithms base new predictions on their historical data processing. Providing the ML solution with more historical data typically results in the tool making better predictions and identifying hidden trends. 

Machine learning algorithms can be categorized into four basic classes based on how they learn to increase their accuracy. The algorithms are chosen by developers and data scientists based on the type of information they want to predict.

  • Supervised learning algorithms are supplied with labeled training data and definitions of the variables they will assess. Data scientists specify the algorithm’s input and output. These algorithms are excellent at data classification and making accurate predictions by using the input of multiple ML models.

  • Unsupervised learning algorithms train using unlabeled data by scanning datasets for meaningful connections. Deep learning solutions often use unsupervised learning for tasks such as anomaly detection and association mining.

  • Semi-supervised learning algorithms combine the previous two methods. The algorithm may be provided with labeled training data but is allowed to explore the information to develop an understanding of it. This type of ML is used in machine translation, labeling data, and fraud detection.

  • Reinforcement learning teaches machines how to complete multi-step procedures that can be clearly defined. An algorithm is initially programmed to perform a task and is given positive or negative reinforcement as it determines the correct steps to complete it successfully. Robotics and resource management make use of this kind of ML algorithm.

How does machine learning improve DLP solutions?

Modern DLP tools make extensive use of ML. The following are some examples of how machine learning can be used to improve DLP solutions.

Identifying sensitive and high-risk data


Machine learning can be instrumental in identifying and classifying the sensitive and high-risk data resources an organization needs to protect. The cloud has complicated the process of categorizing and classifying data elements, and manual processes cannot keep up. 

DLP solutions powered by ML algorithms like Next’s Reveal DLP solution can traverse the computing environment and rapidly classify data elements as they’re created and introduced into the infrastructure. In fact, Reveal is the first DLP agent to deliver machine learning on the endpoint, identifying and categorizing data at the point of risk. 

Automating the enforcement of a data handling policy


ML algorithms can streamline the enforcement of a data handling policy. Learning from prior incidents enables a DLP tool like Reveal to take proactive measures to protect data. For instance, a DLP solution may block attempts to email sensitive materials, encrypt files from a specific application when access is requested rather than when it’s transmitted based on how the information was used in the past, and take other preventive actions based on the company’s policies. Find out how your data loss prevention solution stacks up and validate your DLP policies using our DLP Policy Testing Tool.  

These activities can improve productivity while ensuring sensitive data remains secure. With Reveal’s on-device intelligence, personal data remains on the device rather than being sent to the cloud. Plus, behavior is separated from user identities, so it doesn’t sacrifice employee trust or user privacy.      

Uncovering patterns of unauthorized data usage


Data is everywhere today, from employees’ laptops to the cloud. A DLP solution driven by ML can discern behavior patterns to detect attempted unauthorized access. This information informs necessary changes to security policies and potential threats that require further investigation.  

For example, Reveal begins baselining activity at deployment and leverages multiple behavioral analytics algorithms to differentiate normal activity from anomalous activity. Because it doesn’t require a connection to a separate analytics engine, Reveal enforces policies even when a user isn’t connected to the network.

Offering more effective employee training


One of the features of modern DLP tools is their ability to provide employee training and education to promote a better understanding of the company’s data handling policy. 

Reveal, for instance, sends informative messages to users in response to policy violations, instructing the user on how to avoid this outcome in the future. ML algorithms can learn which types of messaging get the best results and then use that information to tailor the training it provides.

Enhancing your cybersecurity posture

Next DLP’s Reveal data loss prevention platform uses ML to provide complete visibility into a computing environment, no matter how many channels it incorporates. Lightweight agents protect data resources without negatively degrading system performance or user productivity. A cloud-hosted, SaaS solution, Reveal offers out-of-the-box functionality and advanced ML capabilities to provide instant visibility and start protecting your infrastructure immediately.

Reveal’s deep learning ability helps minimize false positives and returns valuable time to your security team. Incident-based education and training also helps everyone in the organization stay on the same page regarding data handling. Book a demo with Next DLP today, and learn how Reveal can protect your enterprise data resources

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