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Can you tell us a bit more about user behavior analytics (UBA) and how it can be used to produce internal threat intelligence? UBA ties data back to an individual and creates a clear picture about what’s happening in an organization, and most importantly, why. Establishing that connection between a user and their action helps to quickly determine where (or with whom) a potential threat originated. Are companies using UBA in conjunction with machine learning and has this approach been successful so far? Absolutely. UBA allows you to generate behavioral details of employees, and machine learning helps navigate that data by filtering abnormal activity that requires attention. We’ve found that customers are looking to solve two main challenges: having enough visibility into their employees and having fast access to relevant data as it’s needed to investigate potential threats.
This combination of technology solves both problems, while reducing the manual work required of their teams. In addition to detecting malicious employee behaviour, can this technology be used to prevent the types of employee negligence that often leads to data breaches? Many companies think that because they have rigorous background checks and seemingly ethical employees, ‘insider threats’ do not apply to them. Data exfiltration often occurs because an innocent person is targeted (through things like phishing attacks) at times when they’ve unintentionally opened the company up to risk.
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Machine learning and UBA can help with fast forensics when something goes wrong, and pinpoint behavioral issues that can be corrected in the future. How do you think employees will respond to their employers using UBA and machine learning to keep a closer eye on their work? While it may feel uncomfortable, many employee contracts already include verbiage about company-issued technology and intellectual property. Network monitoring isn’t – and employees have likely been monitored on the network if they’ve worked inside of an office. UBA and machine learning simply ties this data back to an individual in efforts to reduce the ways a single employee can bring risk to a company.
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(Image: © Image Credit: Devrimb / iStockPhoto) Do you believe more companies will adopt machine learning next year? Yes, however this concept itself isn’t . Ranging from self-driving cars to cybersecurity monitoring, machine learning helps eliminate noise and narrow in on the necessary details. With larger data sets becoming the norm, other algorithms cannot provide the level of advanced analytics needed to create actionable data.
The ability to scale security solutions relies on technologies like this to alleviate work required by human teams, so they can focus on high-impact events. Hani Mustafa, CEO of Jazz Networks ON DECEMBER 27 "360° VIDEO RENDER REVEALS MOTOROLA P40 WILL DITCH NOTCH FOR HOLE-IN-DISPLAY" 360° video render reveals Motorola P40 will ditch notch for hole-in-display The Motorola P-series is a series that was unveiled by the Lenovo-owned company this year. The first of the P-series include the Motorola P30, Motorola P30 Play a.k.a Motorola One, and the Motorola P30 Note a.k.a Motorola One Power. A leak has revealed the series will continue next year, starting with the Motorola P40. The leak is a 360° video render and images courtesy of OnLeaks in collaboration with 91mobiles and they give us our very first look at the device. A signature feature of the Motorola P30 series is the notch above the display.
For the Motorola P40, Motorola is ditching that for what will be the trend of 2019 – a punch hole display. It is too early to say if the other P40 phones will adopt the same design.
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