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Improving Enterprise Cybersecurity With XDREnterprises are looking at eXtended Detection and Response technologies to improve their abilities to detect, and respond to, threats. While endpoint detection and response is not new to enterprise security, organizations have to improve network visibility, expand data collection and expand threat hunting capabilites if they want their XDR deployments to succeed. This issue of Tech Insights also includes: a market overview for XDR from Omdia, questions to ask before deploying XDR, and an XDR primer.
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1 Comments
User Rank: Ninja
6/29/2019 | 2:32:45 PM
One thing I would say about AI, the term is not being used correctly. It is machine learning and not AI. ML is a subcomponent of AI. By definition:
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.[3][4] In its application across business problems, machine learning is also referred to as predictive analytics. - Wikipedia.org.
When we refer to AI, it means the system is self aware and it is able to make decisions without the intervention of a human (it thinks like a human). It can provide an instant response to a threat because it has taken information from numerous resources, created a prioritized depth chart with varying threat percentages from a list of past models and threats. This analysis helps the system determine if it is the same threat experienced by others or a zero day attack. Then it looks into a resolution DB (Deep Learning or Machine Learning) or it identifies areas on the internet as to how to deal with the threat, it communicates that with the human element and rectifys the problem using ML/DL experiences.
I think individuals are mixing the concepts up and not really understanding the differences between the two, a chart has been provided to help individuals understand the differnt between the three areas.