How can machine learning stop email breaches?
Industries across the globe battle email breaches, but what makes them most challenging is that their cause is often simple human error. By focusing security efforts on machine learning solutions, organizations of all sizes can automatically prevent email breaches. This article outlines a brief definition of machine learning, common email mistakes, and how machine learning can help stop outbound email breaches.
How to define machine learning
Machine learning (ML) is essentially the automated process of a computer conducting data analysis to find patterns and make decisions. Ultimately, it powers artificial intelligence (AI) tools and is becoming used in business technology, including customer relationship management (CRM), financial management, and even cybersecurity software.
In the scope of using this technology for cybersecurity purposes, because most breaches are a result of human error, the idea of contextual machine learning makes sense for helping people catch mistakes. Email is a particular area that sees a large pool of mistakes and is a typical source of cyber- incidents.
Email use has always been popular, but it has skyrocketed since the work-from-home model became popularized in the last few years. Data from our 2021 Data Loss Prevention Report showed 85% of remote employees were sending more emails than before the pandemic. As a result, more breaches or other incidents occur from constructing and sending email messages.
Solutions using machine learning are the future, as they can stop mistakes in real time by thoroughly understanding human behavior.
Common email mistakes
Before going into the "how" of machine learning for email security, it's essential to understand peoples' common mistakes, like adding an incorrect or unnecessary recipient to the To, Cc, or Bcc boxes.
While most email messages we send are likely not going to cause any issues, there are scenarios where we could be emailing sensitive data that results in severe reputational or financial harm. For instance, sending employee or customer personally identifiable information (PII), login credential data, or company trade secrets could mean giving access to someone who may act on it with malicious intentions.
Next is the issue of sending the wrong file attachments. You may find yourself trying to email a non-sensitive file like DraftforBlogArticle.docx and accidentally add EmployeeTaxInformation.xlsm simply because of the alphabetical file order.
Things become even more chaotic if you would send a mass email to dozens or even hundreds of recipients. Once that happens, you have zero control over where that file goes. The issue doesn't necessarily have to be that your recipients are malicious, but they could also be negligent, and those files could end up in the wrong hands.
Machine learning for email security
Machine learning helps to support elaborate data loss prevention (DLP) mechanisms. By constantly collecting contextual data about the email user, such as relationships with recipients and types of content or files frequently shared, a machine learning tool can know more about their behavior than they know about themselves.
Machine learning becomes a powerful DLP tool when combining behavioral analysis with organizational-security barriers. That could include policies about which confidential information can be sent via email and general parameters such as detection for large recipient lists. Machine learning platforms look for anomalies or unusual circumstances to activate email-security protocols using the preconfigured guidelines and contextual behavioral analysis.
For example, suppose the ML tool analyzed your email activity data for a few months. The only items you usually send are marketing brochures and digital content drafts to your manager for review and occasional personal data files to the HR department. You accidentally add a spreadsheet file with every company device's IP address one day. Additionally, you double-clicked on the Cc box by mistake and added one of your suppliers to the email.
The ML tool detected both of those as unusual circumstances based on your past behavior as you don't usually add those types of files AND don't ever email suppliers. Before sending the message, the ML tool prompts a message notifying you that you have added a file and contact that you don't typically add. This security system gives you that added layer of DLP email security.
Time to prevent email data leaks
Egress Prevent is a best-in-class data loss prevention tool that utilizes contextual machine learning to stop accidental and intentional email data breaches. This technology allows your organization to pinpoint email security risks, protects users from emailing incorrect attachments, and stops the wrong recipients from receiving email messages. It even integrates with Microsoft Azure Information Protection so you can label sensitive files for the machine learning platform.