| | 9CIOReviewDECEMBER 2020fears do not seem so far-fetched. All aspects of cybersecurity ranging from data center protection to endpoint security are moving at an accelerated pace to leverage ML to defend against the opposition. Blue Team OperationsWhen wespeak toexecutives fromorganizations that support the blue team, such as Optiv Security, Cyber Security Expertsand Birch Cline Technologies,we find a healthy and promising divergence. Since the very beginning of cyber blue team planning and operations the perspective has been one of a defensive posture. We have come to understand that this dichotomy is being transformed from a defensive approach to an offensive one because of ML. Cybersecurity frameworks like National Institute of Science and Technology (NIST) 800-53 require framework adopters to establish a baseline configuration for the computer systems that comprise an organization. Organizations that complete this task can layer in ML-based security controls that can detect, identify, respond and protect against attacks much faster than a Security Analyst. Unlike the red team restrictions on program code and algorithms the blue team is situated nicely with these assets, as well as vendors, software coders and executives eager to purchase the technology. As such, we have the formation of an edge for the blue team. With ML baseline pattern and behavioral anomaly technology deployed at the network edge, at core, distribution and access layers we have a formidable security posture that would thwart the most common attack, which is incredibly encouraging for the blue team.Our analysis of the impact of AI and ML on cybersecurity security suggests in the blue team security capability will outpace that of the red team. However, it should be noted that without a healthy appreciation and implementation of cybersecurity basics, ML advantages will be much harder to realize. Before we go overboard with AI and ML branded solutions, let's be sure to first adopt a security framework, have a credible security firm perform a risk assessment with gap analysis and create a 12 to 18month remediation roadmap. As we go through the prioritized remediation roadmap, we can layer in ML technologies at the appropriate time. This is the most responsible approach for developing an enterprise cybersecurity posture and risk management program where ML technology benefits can be most effective.ConclusionThe human brain has the capability to process 400 billion bits of information per second that equates to 40 GB/second. NVidia debuted the smallest AI supercomputer in February of 2019 called the Jetson Xavier NX for $399. The NX can process 51.2 GB/second. At about the same time, researchers at the Korea Advanced Institute of Science and Technology (KAIST), the University of Cambridge, Japan's National Institute for Information and Communications Technology (NICT), and Google DeepMind headlined with "Brain code can now be copied for AI, robots." It seems evident that for $399, one can obtain more raw computing capability than the human brain. Until R&D is perfected to mirror the reasoning, emotional and cognitive capacity of the brain, humankind remains at the top of the evolutionary order. At the current rate of change and technology advancement, it remains to be seen how much longer we retain our superiority! We see advancements across just about every business vertical as a direct result of the computer
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