Being a cybersecurity analyst at a large company today is a bit like looking for a needle in a haystack -- if that haystack were hurtling toward you at fiber optic speed. Every day, employees and customers generate loads of data that establish a normal set of behaviors. An attacker will also generate data while using any number of techniques to infiltrate the system; the goal is to find that "needle" and stop it before it does any damage. The data-heavy nature of that task lends itself well to the number-crunching prowess of machine learning, and an influx of AI-powered systems have indeed flooded the cybersecurity market over the years. But such systems can come with their own problems, namely a never-ending stream of false positives that can make them more of a time suck than a time saver for security analysts.

Although it dates as far back as the 1950s, Artificial Intelligence (AI) is the hottest thing in technology today. An overarching term used to describe a set of technologies such as text-to-speech, natural language processing (NLP) and computer vision, AI essentially enables computers to do things normally done by people. Machine learning, the most prominent subset of AI, is about recognizing patterns in data and computer learning from them like a human. These algorithms draw inferences without being explicitly programmed to do so. The idea is the more data you collect, the smarter the machine becomes.

Although it dates as far back as the 1950s, Artificial Intelligence (AI) is the hottest thing in technology today. An overarching term used to describe a set of technologies such as text-to-speech, natural language processing (NLP) and computer vision, AI essentially enables computers to do things normally done by people. Machine learning, the most prominent subset of AI, is about recognizing patterns in data and computer learning from them like a human. These algorithms draw inferences without being explicitly programmed to do so. The idea is the more data you collect, the smarter the machine becomes.

Although it dates as far back as the 1950s, Artificial Intelligence (AI) is the hottest thing in technology today. An overarching term used to describe a set of technologies such as text-to-speech, natural language processing (NLP) and computer vision, AI essentially enables computers to do things normally done by people. Machine learning, the most prominent subset of AI, is about recognizing patterns in data and computer learning from them like a human. These algorithms draw inferences without being explicitly programmed to do so. The idea is the more data you collect, the smarter the machine becomes.

Although it dates as far back as the 1950s, Artificial Intelligence (AI) is the hottest thing in technology today. An overarching term used to describe a set of technologies such as text-to-speech, natural language processing (NLP) and computer vision, AI essentially enables computers to do things normally done by people. Machine learning, the most prominent subset of AI, is about recognizing patterns in data and computer learning from them like a human. These algorithms draw inferences without being explicitly programmed to do so. The idea is the more data you collect, the smarter the machine becomes.

A new artificial intelligence (AI) SaaS application was introduced at the RSA Conference 2017 courtesy of PatternEx. PatternEx is making the application available as a free trial to enable selected customers to see just how good it really is. AI is a fantastic tool and one that will be used in almost everything soon, and now experts are using it to detect cyber attacks in real time. The new Threat Prediction Platform by PatternEx is human-assisted. It's been proven that this approach produces around five times less false positives while at the same time detecting ten times more the attacks than other detector solutions.

An artificial intelligence engine can do a much better job of detecting security threats when it has a little help from a human, according to Kalyan Veeramachaneni, principal research scientist, Laboratory for Information and Decision Systems at MIT. "Unsupervised learning is not enough," he said during a presentation at SWIFT's Sibos conference in Geneva. A security analyst can play a key role in identifying security threats that computers and data scientists might miss, because they aren't experts in security. Collaborating with PatternEx, a start up in the infosec space, Veeramachaneni set out to build an interactive system that would get feedback from a security analyst through a supervised learning model. "We are replicating what an analyst would say -- we call it the virtual analyst." The model captures the knowledge of a security analyst and tries to predict whether activity constitutes an attack, something he called an augmented system.

Cyberspace is an increasingly hostile environment. In 2015, a PwC study of U.S. organizations found that 79 percent of respondents had detected a security incident during the year. Today, malicious hackers continue to wage on business networks and systems. Their aim - to extract data to sell on the black market. Making use of the latest technology, these criminals exert huge pressure on businesses to defend its assets.

Ben Dickson is a software engineer and the founder of TechTalks. Computers and mobile devices running rich operating systems have a plethora of security solutions and encryption protocols that can protect them against the multitude of threats they face as soon as they become connected to the Internet. Such is not the case with IoT. Of the billions of IoT devices presently in use, a considerable percentage are sporting low-end processing power and storage capacity and don't have the capability to become extended with security solutions. Yet they are connected to the Internet, nonetheless, which is an extremely hostile environment.

This vendor-written tech primer has been edited by Network World to eliminate product promotion, but readers should note it will likely favor the submitter's approach. Everyone says there is an information security talent gap. In fact, some sources say the demand for security professionals exceeds the supply by a million jobs. Their argument is basically this: attacks are not being detected quickly or often enough, and the tools are generating more alerts than can be investigated, so we need more people to investigate those alarms. We believe that, even if companies aroaund the world miraculously hired a million qualified InfoSec professionals tomorrow there would be no change in detection effectiveness and we would still have a "talent gap."