() is all over the technology headlines lately. It seems to be the latest buzzword to take hold, yet the question remains: Will this be a quick fad, or are we actually seeing the second coming of ?
Most recently, there has been a heavy surge of thinking applied to cybersecurity. Could this be a long-term application for the Cognitive Technologies are another piece to the artificial intelligence puzzle. The technologies are able to perform tasks which until recently required a human. The said tasks can be anything from computer vision, to speech recognition or robotics. and a way for enterprises to ward off some of the increasingly stealthy attacks they are blind to at the moment? Will cybersecurity give new life and a second chance in an industry desperate to provide protection against cyberthreats?
A Quick History Lesson
Before we dive into the viability of in cybersecurity, let’s review a little bit of its history . is certainly not a new concept; rather, it’s one that has been in development since the early 1950s. The first attempt at what we would all recognize as was in 1950 when researcher Alan Turing constructed a test in the 1950's to determine a machine's intelligence. The test is considered to be passed, when a human cannot tell whether s/he is interacting with a machine or a human. The test as sparked controversy but is an important concept nevertheless. published ” Computing Machinery and Intelligence ,” where he proposed an imitation game dubbed the “Turing test.” Since that time, we’ve seen interesting applications of the technology — from the first human-like , Honda’s ASIMO , to IBM’s Watson , the supercomputer that went on to defeat the two greatest Jeopardy champions in the TV show’s history. But as of yet, we have not seen a wide application of artifical intelligence or machine in a business setting.
The Case For In Cybersecurity
What problem could machine and solve for cybersecurity? The answer to that question is twofold. The lack of visibility is one of the biggest cybersecurity challenges that enterprises face today. IT security teams are struggling to see what is happening in and around their IT infrastructures. They are challenged to sift through the numerous security incident logs, to detect potential threats and to be able to catch, stop or mitigate them. They struggle to understand where all corporate data lives and who has access to it, not to mention what users are doing with that access. The logs generated by their security and identity governance infrastructures are not by the thousands, nor by the millions, but by the hundreds of millions on a weekly basis. To spot the clue for an attack or any abnormal, suspicious activity would require more than a very large IT team.
There is clearly a Big Data describes data collections so big that humans are not capable of sifting through all of it in a timely manner. However, with the help of algorithms it is usually possible to find patterns within the data so far hidden to human analyzers. problem in security. There is just too much data for any enterprise IT team to manage, bare minimum. But to also be able to analyze all of that data, seeking out anomalies that could signal a looming threat, is another story entirely. Even if an enterprise IT team had enough manpower to manage the data at hand, there is just no way for us as humans to analyze that much data in real time. With machine , that mountain of data could be whittled down in a fraction of the time, helping organizations quickly identify and then mitigate a security incident. could be a game-changer for security teams.