AI Automating Cybersecurity in Post-COVID World: Wedge can get you straight to Level 5…

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A great article in Venturebeat was recently published that brought up the marked acceleration of using AI in the realm of cybersecurity.  Unfortunately, cybercriminals are ahead of the curve in this regard and are evolving much more quickly in order to take advantage of the situation.  Cybercriminals have already started looking at artificial intelligence to help increase the breadth and depth of their attacks.  They have already begun utilizing AI to allow for faster cracking of passwords and to even automate their attack infrastructure.  With cybercriminals utilizing AI to their benefit, the question becomes, “What about the other side?  How can the Good Guys Utilize AI to defend against these attacks?”  

Venturebeat provided a nice little chart on the Levels of Cybersecurity Automation, which outlined the following:

Level 1 – Manual Detection & Manual Response – Events are detected by Security Operations Center (SOC) and fraud teams; manual mitigation of individual events.

Level 2 – Manual Detection & Semi-Automated Response – Responses are sufficiently well-understood that automation through rules and scripts is used in response and mitigation.

Level 3 – Semi-Automated Detection & Semi-Automated Response – Event detection facilitated by automated analytics; typically where machine learning (ML) is introduced.

Level 4 – Automated Detection & Semi-Automated Response – Event detection is fully automated through ML and other analytics, driving SOC & fraud workflows, investigations, and rule generation.

Level 5 – Fully Automated Detection and Fully Automated Response – Detection and response systems are fully automated for all events; manual effort is primarily focused on improving the automated system as opposed to dealing with individual events.   And this is where Wedge Networks can take you!

Unfortunately, most organizations are at Level 1, with some more sophisticated organizations in the Level 3 and 4 range. In order to adequately defend against the cybercriminals in the long term, organizations are going to have to be at Level 5, where AI can help to overcome the Security Gap; which is the gap in which the finite network security resources are quickly overrun by the exponentially expanding security threats.  Without Level 5, resources will continually be spent trying to deal with individual events, instead of focused on improving the security as a whole.. As the cybersecurity industry continues to further integrate AI and machine learning into solutions, it is boiling down to more of an analytics and automation problem; one that can often be plagued with both false positive and false negative issues that need to be worked out.  More false positives and customers are unhappy, more false negatives and successful attacks increase, and customers are again unhappy.

So, with all of the above, where is YOUR organization at in terms of AI integration?  Does your cybersecurity solution utilize AI and machine learning?  If not, why not?  If they do, to what level is it integrated?  

The great things is that Wedge saw the writing on the wall several years ago and did something about it.  We’ve actually been integrating Ai and machine learning into our Wedge Absolute Real-time Protection (WedgeARP) platform for several years now and with great success!  We have been orchestrating AI at the network level with our Wedge Advanced Malware Blocker, which revolutionized malware PREVENTION.  WedgeAMB orchestrates AI technology and other technologies to detect and block both known and unknown (never-before-seen) malware, preventing them from entering the network and stopping attacks before they can start.  Combined with WedgeIQ and the actionable threat analytics that it provides, Wedge has been at the forefront of AI utilization in the cyber security realm.  

And, we’ve not stopped improving!  Wedge has further devised Wedge Nucleation, which is an automated continuous deep learning process for real-time threat prevention.  This process uses automation for continuously training artificial neural networks (ANN) from filtered and labeled network data.  With each iteration, the ANN further improves its accuracy of filtering and labeling network data, adding to the ever-evolving intelligence.  This intelligence further reduces both false positives and false negatives, leading to much more accurate results.  To learn more about how Wedge has been utilizing AI in its real-time threat prevention platform, contact our team at:  It’s time that organizations jump ahead of the cybercriminals and protect themselves against the new generation of AI-based attacks.  Wedge Networks can enable your organization to do just that!

About Wedge Chief Scientist

Husam Kinawi, Chief Scientist Dr. Kinawi has a PhD and MSc in Computer Science from the Universities of Calgary, Canada and London, UK. In 1997, he co-founded Mpower Technologies Inc., a wireless telecommunications software company. In 1999, Dr. Kinawi co-founded (NASDAQ: AIQT), a Boston-based e-Business applications firm. Dr. Kinawi has over seventeen years of research and development experience working with industry leaders such as Newbridge (Alcatel), Siemens, United Technologies, and Apple in the areas of distributed information systems, embedded applications and wireless Internet solutions. Dr. Kinawi has also spoken at several major conferences, published several research papers, and is the holder of several patents in the area of mobile and wireless devices.
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