Finally the time is here for Artificial Intelligence and Machine Learning (AIML) in cyber. The industry is recognizing the power and the value of AIML and is finally making investments in this space.
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When progressive technologies start to deliver on their potential, we can expect a wholesale shift of vendors looking to get on the bandwagon. First the technology enthusiasts and early adopters will come to validate the promises of the newest technology and hone its potential into something viable for the mainstream. Once that is done, the early majority, late adopters and finally, even the skeptics jump in as well.
Finally the time is here for Artificial Intelligence and Machine Learning (AIML) in cyber. There is a widespread move out of the early adopter stage and into the early majority stage of adoption. We need to get onboard if we are going to thwart cybercriminals. The good news is that the industry is recognizing the power and the value of AIML and is finally making investments in this space.
It is difficult not to see the viability of AIML given how visible the technology has become in other mission-critical services as some of its most promising use cases have matured through stages of development to deployment in recent years. For example, the technology Nissan , Ford and Volkswagen by 2022 or the use of diagnostic tools like IBM’s Jeopardy! -winning Watson in hospitals around the US , are highly monitored and anticipated mission-critical use of AIML in respective industries.
However, there are use cases that are making a more dramatic impact than realized, from the deployment of in supply chain and warehouse management, as Amazon has already done , to the widespread adoption of automated journalism, with major publications from the Associated Press to the Los Angeles Times relying upon to deliver sports journalism, weather updates and even breaking news stories in a far more timely manner than their human counterparts.
How, then, is used to deliver such varied use cases across a range of industries? The answer lies in what AIML does best: ingesting mass amounts of data to recognize patterns at a scale beyond human ability. Given this, it makes sense that is rapidly being considered and studied within the field of cybersecurity.
Taking it one step further, analyzing the behavior of the user into account is also key when it comes to . While anybody can train an to detect anomalies, anybody can likewise train an to trick such a system. But human behavior is unique to the individual and is very difficult to mimic the human behavior of an individual. When is paired with technologies such as , it deduces simplified models of human behavior that are capable of assessing, even predicting future actions.
How AIML Changes the Game for Bad Guys
It is very complex to mimic the human behavior of an individual. It is that deduces simplified models of human behavior that are capable of assessing, even predicting future actions.
AIML is already being taken advantage of by the most opportunistic of cyber experts, hackers. Bad actors understand perhaps more than anyone the value in being able to adapt to specific systems at a grand scale. The same technologies that empower computers to make guided decisions when say, managing millions of potential outcomes in Go — a game with many more potential variations than chess – also empowers hackers to develop smarter phishing methodology which could alter its messaging based on collected data, allowing the hacker to personalize attacks for a greater chance of success at a much more rapid pace. Once in your system, adds another level of risk in the form of Smart malware, which can be taught to sit in a system undetectably until the program deems certain parameters have been met — whether that’s when logging into a given system or even when hearing a predetermined voice-activated trigger. As hackers are enabled to better make their way into protected systems, Role-based access control (RBAC) will no longer be enough. […]