Machine learning techniques can assist increase the security of any mobile device, making them acceptable as employee IDs and removing the need for easily hacked passwords. Endpoint security must be hardened to the mobile device level as part of any organization's Zero Trust Security initiative today.
Fremont, CA: Mobile devices are attractive to hackers because they are built to provide speedy answers based on little contextual information. According to Verizon's 2020 Data Breach Investigations Report (DBIR), hackers are succeeding with integrated email, SMS, and link-based social media attacks focused on acquiring passwords and privileged access credentials. And, with such a growing number of breaches originating on mobile devices, according to Verizon's Mobile Security Index 2020, and 83 percent of all social media visits in the United States occurring on mobile devices, as per Merkle's Digital Marketing Report Q4 2019, applying machine learning to harden mobile threat defense should be at the top of any CISO's priority list right now.
Google's use of machine learning to combat the onslaught of phishing assaults during the Covid-19 outbreak provides insight into the magnitude of these dangers. Gmail blocks 100 million phishing emails every day. Google's G-Mail Security team saw 18M daily malware as well as phishing emails related to Covid-19 during a week in April of this year. Google's machine learning models are growing to understand and filter phishing threats, and they have effectively blocked more than 99.9 percent of spam, phishing, and malware from reaching Gmail users. Microsoft thwarts billions of phishing attempts on Office365 alone each year by leveraging heuristics, detonation, and machine learning, which Microsoft Threat Protection Services bolster.
According to a recent research report, 42 percent of the US labor force currently works from home (SIEPR). The bulk of people who work from home are in professional, technical, or managerial professions, and they rely on various mobile devices to complete their tasks. The ever-increasing number of threat surfaces that all enterprises must deal with nowadays is the ideal use case for preventing phishing efforts at scale.
Machine learning techniques can assist increase the security of any mobile device, making them acceptable as employee IDs and removing the need for easily hacked passwords. Endpoint security must be hardened to the mobile device level as part of any organization's Zero Trust Security initiative today. The good news is that machine learning algorithms can thwart hacking efforts that interfere with employees' mobile device IDs, expediting system access to the resources they require to get work done while remaining secure.
It takes more than after-the-fact analytics and KPIs to keep enterprise-wide cybersecurity efforts focused; what is required is look-ahead predictive modeling-based machine learning data acquired at the device endpoint. The future of endpoint resilience and cybersecurity must begin with the device. Capturing data at the device level in real-time and using it to train algorithms, together with phishing URL search, Zero Sign-On (ZSO), and a built-in Zero Trust approach to security, are critical for defeating today's increasingly complex breach attempts.