These days, it seems like there’s a new way to engage with the virtual world each and every day.
Whether you’re a business owner selling products to clients across the world, a night student looking to get a second degree, or an influencer building a social media empire, it’s likely that you find yourself intertwining your offline and online life more and more as our day-to-day happenings shift into an online space. This engagement opens new doors to education resources, business opportunities, and entertainment outlets that have completely revolutionized the way the world works.
However, with this new wave of technology that we’ve seen in the past few years has come a new wave of security breaches, malicious online behavior, and other forms of cyberattacks. Whereas just a few years ago, it would’ve been unthinkable to share sensitive personal information online and over the phone, it is now the norm to manage personal and corporate finances through banking apps and share sensitive personal information, like social security numbers and routing numbers, online as the globe transitions to remote employment.
With all these upgrades to the technology we use every day, people need to upgrade to our antivirus (AV) software as well. That’s where next-generation antivirus (NGAV) comes into play: a way of anticipating threats through the use of artificial intelligence and machine learning.
AV Versus NGAV
The main difference between traditional AV and NGAV is the approach each takes when trying to identify viruses and malware.
Traditional AV focuses on the way that files look with the assumption that malware has a specific type of signature. This is called signature-based detection. When files are incoming into a computer system, traditional AV compares these with a database of known malware and if there are similarities in the signatures, the AV flags the incoming file as a potential cyberattack.
NGAV, on the other hand, is much more proactive and preventative in its approach, taking in the whole context of a system and analyzing behaviors rather than isolated endpoints to find vulnerabilities within a system. Traditional AV is unable to detect fileless attacks and ransomware and often alerts a user after the fact of an attack. With the constant behavioral analysis inherent to NGAV functionality, these more complex attacks can be recognized and taken care of before the breach occurs.
The idea behind this software is that viruses, malware, and ransomware all behave in certain ways that can tip off NGAV software to their true identity as malicious entities. The machine learning aspect of NGAV software allows it to compare behaviors across thousands of identified and unidentified assets in live time to assess trends, analyze behavioral patterns, and, as a result, identify modern, complex attacks.
Electronic Detection Response (EDR)
You might’ve heard the term EDR thrown around in conversations about NGAV. This is for good reason because the two share many similarities and are certainly tied up within one another. Many experts are now calling for a joint NGAV-EDR approach to security software for the most protection possible.
Similar to NGAV, EDR uses the power and modernity of machine learning and artificial intelligence to merge real-time monitoring of endpoints with data comparison to identify behavioral patterns that are suggestive of malware. When used in conjunction with NGAV, your system will be all the more protected from complex attacks.
Extended Electronic Detection Response (XDR)
XDR is the newest in NGAV and goes beyond just focusing on endpoint detection, like EDR. With XDR, the focus is on multiple data sources at once without being limited to endpoints or signatures and it takes a more comprehensive approach to identify suspicious behavior patterns at every point of a virus’s life. The main perk of XDR is the efficiency and simplicity of its software, meaning security teams won’t be thinly spread and, thus, more capable of protecting your systems from malware attacks.