Read a security related press release or been to an event recently? You\u2019ve no doubt been wondering how you managed to do your job all this time without Artificial Intelligence (AI) or Machine Learning (ML).Do these technologies really live up to the hype or are they just the latest in a series of new buzzwords?NOW AVAILABLE: Industry\u2019s First Machine Learning Incident Response Platform that Gets Smarter with Every Analyst Action!Despite being positioned as the latest \u201csilver bullet\u201d in security, neither are new concepts. Artificial Intelligence, which in layman\u2019s terms is simply making a computer think like a human, was first discussed at a Dartmouth Summer Research Program in 1956. Similarly, Machine Learning, which is broadly considered a type of Artificial Intelligence and is defined as giving computers the ability to learn without explicit programming, was pioneered by an IBMer named Arthur Samuel in 1959.Though decades old, Artificial Intelligence and Machine Learning are both garnering interest in the field of cyber security. Recent research by ESG surveyed 412 cybersecurity professionals to assess and characterize their knowledge of Artificial Intelligence and Machine Learning as it relates to cybersecurity analytics and operations. The findings show a confusion in the market which is no surprise given the uprise in promises made by vendors.Two interesting, yet conflicting stats that I noticed in the ESG research are that although 70% don\u2019t understand where Machine Learning and Artificial Intelligence fit in their organization, 82% plan to deploy it! Clearly we have an opportunity for education.Artificial Intelligence is a broad term and represents technologies with many approaches, from simply creating rules to handle specific tasks, to highly-sophisticated algorithms that learn correct behavior. Machine Learning is thought to be the most promising form of Artificial Intelligence. Machine Learning uses algorithms and data to learn without being explicitly programed. This corrects a major limitation with other forms of Artificial Intelligence where rules must be created to handle specific tasks requiring foresight and programing for all possible outcomes in advance. There are many forms of Machine Learning including Decision Tree Learning, Inductive Logic Programming, Deep Learning, Clustering, and others like Reinforcement Learning.Security Automation & Orchestration platforms are beginning to use Reinforcement Learning, which is a simple form of Artificial Intelligence (and Machine Learning) that automatically determines the actions required to get the best outcome. In the context of SA&O platforms, Reinforcement Learning can make recommendations based on event data, ultimately suggesting automation playbooks that can help solve real problems in the SOC. Guidance when dealing with \u201cknown unknowns\u201d (i.e. those cases when we know about the threat, but aren\u2019t sure how to respond) is valuable to new and experienced analysts alike.Though we\u2019re easily enamored with new technologies like Artificial Intelligence, Machine Learning, or even Reinforcement Learning, it\u2019s always useful to step-back and ask the bigger question. How do any of these new technologies help us solve real problems like reducing our Mean Time to Resolution (MTTR)?The reality is that no one technology provides the \u201csilver bullet,\u201d each merely adds another dimension to the solution. While perhaps not as fresh to the market narrative, foundational capabilities like architectural maturity, community collaboration, an open & extensible ecosystem, and feature completeness often do more to make an impact than the \u201clatest thing.\u201dThat\u2019s not to say artificial intelligence, machine learning, reinforcement learning, etc. don\u2019t have a place. I think they\u2019ll play an increasingly important role in the future in providing guidance to an analyst that enables a new level of security handling, one where threats with no associated procedures can be handled effectively through intelligent guidance.Let\u2019s not get carried away though. Artificial intelligence, machine learning, reinforcement learning are great ways to augment \u2013 though not outsmart\u00a0\u2013 the analyst.