Is the future of cyber security machines versus machines? As hackers increasingly use automation and machine learning to launch cyber attacks at scale, cyber security defenders, too, are turning to artificial intelligence to detect hacks -- and, in some cases, kill them dead automatically. But the use of AI for cyber defence is still nascent, according to many experts, and must be deployed with care. Some argue there is a tendency for the cyber security industry to exaggerate AI's potential and successes, and use it as a buzzword. "Having a fully automated system in the cyber security domain would mean essentially trusting the computer with decisions. "There are critical things that would be hugely costly if done incorrectly," he says. "It's a question of'how accurate is this thing relative to the human?' And, in the cyber security domain, it's just simply'not very'." So how far along are we? Already, cyber security companies are using AI to help detect potential attacks by flagging suspicious behaviour. Justin Fier, vice-president of tactical risk and response at Darktrace, says the UK-based company uses "various forms of machine learning to go into your digital estate and, quite simply, establish a sense of self, establish what is specific to an organisation". He adds: "The minute something deviates -- big or small -- we can actually alert you to that." Darktrace also has automated responses to known threats such as ransomware strains. "Now, the median time to detect and remediate ransomware is 45 minutes.

In today's world, everyone is benefitting from internet connectivity. Just a click is enough to know about the whereabouts of a distant friend or the location of aliens(I know you cannot). But at the same time, We as a user are at risk to savvy cyber criminals who are constantly adapting their techniques to sneak into critical systems. As an internet user, there is always a limitation on what you want to tell people about yourself publicly. Imagine you wake up and find that everyone knows about your medical condition that you wanted to keep private.

Vectra is the leader in AI-driven threat detection and response for hybrid and multi-cloud enterprises. The Vectra Platform captures packets and logs across network, public cloud, SaaS, and identity by applying patented security-led AI to surface and prioritize threats for rapid threat response. Vectra's threat detections are powered by a deep understanding of attacker methods and problem-optimized AI algorithms. Alerts uncover attacker methods in action and are correlated across customer environments to expose real attacks. Organizations around the world rely on Vectra to see and stop threats before a breach occurs.

Artificial intelligence is a scientific field that is responsible for finding solutions to complex problems that humans do not have. Machine learning could be used to bypass and dismantle cyber-security systems faster than most prevention and detection tools can keep up. AI will exacerbate existing threats and create new ones, but its speed could prove a great boon for cybercriminals, as it is much more effective at fighting them than human experts. The algorithm is attempted to model a decision mechanism that resembles real human decision mechanisms but is modeled by algorithms. In the context of cybersecurity, artificial intelligence (AI) tries to defend the system by weighing patterns of behavior that indicate a threat against predictive logic.

My passion for security came to the forefront when I ran the Security business at Juniper. Given this confluence of factors, the opportunity to apply Artificial Intelligence (AI) to dramatically innovate how we could detect and respond to advanced attacks (the new norm), created an incredible opportunity to build a next-generation security company. Which directly led to the creation of Vectra. Digital Transformation can become a task rife with complexities and it is no surprise that security risks become a byproduct of that. Effective Network Detection and Response (NDR) provides visibility that simplifies complexity, replacing it with the confidence that a customer's security capabilities will enable, not inhibit, their journey – legacy methods disproportionately focused on prevention are brittle and frankly designed in such a way that makes this journey needlessly costly and difficult.

In this Help Net Security podcast, Chris Morales, Head of Security Analytics at Vectra, talks about machine learning fundamentals, and illustrates what cybersecurity professionals should know. Hi, this is Chris Morales and I'm Head of Security Analytics at Vectra, and in this Help Net Security podcast I want to talk about machine learning fundamentals that I think we all need to know as cybersecurity professionals. AI has become very used within our industry more and more, and here at Vectra we are an AI company as well. As you start to hear more about AI, you have to start asking yourself what is it really, what makes a machine intelligent and in the next ten minutes I just want to give a quick overview so that you can understand some of the principle operations and applications of how machine learnings apply to build AI, and just kind of a quick understanding of the different algorithms or understanding when you need to use certain algorithms for specific jobs. There has always been a very muddled use of the terms artificial intelligence, data science and machine learning.

Security startup Vectra closed a $36 million Series D funding round, bringing its total funding to $123 million. The company will use the latest investment to help build a research and development center in Dublin as it pushes its artificial intelligence (AI)-based platform into the global market. Atlantic Bridge led the Series D round. The Ireland Strategic Investment Fund (ISIF) and Nissho Electronics Corp. also participated along with returning investors Khosla Ventures, Accel Partners, IA Ventures, AME Cloud Ventures, DAG Ventures, and Wipro Ventures. The funding comes as a new report from McAfee, in partnership with the Center for Strategic and International Studies, found cybercrime costs businesses close to $600 billion per year.

Recent breakthroughs in machine learning and artificial intelligence mean AI-enabled technologies are gaining traction. The billion-dollar cybersecurity industry is no exception, as vendors begin to scale and automate their processes intelligently - all while locked into the early stages of a security arms race with professional hackers. A recent report from analyst firm ABI Research estimates that machine learning in cybersecurity will enormously bolster spending in big data, intelligence and analytics, reaching as much as $96 billion (£71.9 billion) by 2021. Vendors are likely to find buyers in large enterprises, and more than likely, across industries that are especially prone to attack: think government and defence, banking, and across the technology sector. At the moment, ABI's report says, User and Entity Behavioural Analytics - using machine learning for threat detection by analysing data at scale - is the driving force. "Using static machine learning models to detect previously unknown malware is the only use case I'm aware of that offers clear evidence of effective results," says cybersecurity analyst at 451 Research, Adrian Sanabria.

Recent breakthroughs in machine learning and artificial intelligence mean AI-enabled technologies are gaining traction. The billion-dollar cybersecurity industry is no exception, as vendors begin to scale and automate their processes intelligently – all while locked into the early stages of a security arms race with professional hackers. A recent report from analyst firm ABI Research estimates that machine learning in cybersecurity will enormously bolster spending in big data, intelligence and analytics, reaching as much as $96 billion (£71.9 billion) by 2021. Vendors are likely to find buyers in large enterprises, and more than likely, across industries that are especially prone to attack: think government and defence, banking, and across the technology sector. At the moment, ABI's report says, User and Entity Behavioural Analytics – using machine learning for threat detection by analysing data at scale – is the driving force. "Using static machine learning models to detect previously unknown malware is the only use case I'm aware of that offers clear evidence of effective results," says cybersecurity analyst at 451 Research, Adrian Sanabria.

Security is the gift that keeps on giving. There's always one more vulnerability to be exploited, one more household-name company that's about to be breached. So, it shouldn't be surprising that 2016 offered up some new lessons -- or, in some cases, things the industry already knew but needed a reminder about. It isn't exactly the rise of the robots, but the Mirai botnet proved that the Internet of Things (IoT) is easily exploitable. This is a possibility that's been lurking all along, but in 2016, it became real.