Machine learning is the latest to make waves in the field of Information Security, and for good reason. The support of complex algorithms that'learn' and grow is invaluable to human analysts, allowing them to focus on larger tactical fights and strengthen security systems to be virtually bulletproof. In both routine and structural changes to Information Security, machine learning plays an increasingly important role and will continue to do so, leading into the coming years. What is Information Security (InfoSec)? InfoSec refers to the systems, tools and processes that are designed and then deployed to field sensitive and confidential data from being compromised or tampered with.

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We use artificial intelligence (AI) every day without knowing it. Alexa's speech recognition, Netflix's movie recommendations and Gmail's type-ahead suggestions are all examples of data you share being fed into deep learning AI models to improve your experience. At work, the same technology is increasingly used to deliver smart services -- rooms that book themselves, thermostats that don't cool empty floors and outages that are restored before anyone knows they exist. Achieving Amazon-quality AI with "small data" from your organization is a more complex technical problem. To get enterprise AI right at scale requires thinking differently about how applications and services are deployed and managed.

In this Spotlight Podcast, we speak with David Brumley, the Chief Executive Officer at the security firm ForAllSecure* and a professor of Computer Science at Carnegie Mellon University. Brumley is a noted expert on the use of machine learning and automation to cyber security problems. In this podcast, we talk about the growing demand for security automation tools and how the chronic cyber security talent shortage in North America and elsewhere is driving investment in automation. Every so often, a technology comes along that seems to perfectly capture the zeitgeist: representing all that is both promising and troubling about the future. In the 1960s, you think of plastic, which was a pillar of a massively expanding consumer culture in the United States that put "convenience" above all else.

Heading into 2018, some of the most prominent voices in information security predicted a'machine learning arms race' wherein adversaries and defenders frantically work to gain the edge in machine learning capabilities. Despite advances in machine learning for cyber defense, "adversaries are working just as furiously to implement and innovate around them." This looming'arms race' points to a larger narrative about how artificial intelligence (AI) and machine learning (ML) -- as tools of automation in any domain and in the hands of any user -- are dual-use in nature, and can be used to disrupt the status quo. Like most technologies, not only does AI and ML provide more convenience and security as a tool for consumers, but each can be exploited by nefarious actors as well. A joint publication released today by researchers from Oxford, Cambridge, and other organizations in academia, civil society, and industry (including Endgame) outlines "the landscape of potential security threats from malicious uses of artificial intelligence technologies and proposes ways to better forecast, prevent, and mitigate these threats." Unfortunately, there is no easy solution to preventing and mitigating the malicious uses of AI, since the tools are ultimately directed by willful actors.

When you search for security data science on the internet, it's difficult to find resources with crisp and clear information about the use cases, methods and limitations in Information Security (hereby referred to as InfoSec). There's usually always some marketing material attached to it. So, I thought of summarising my knowledge and InfoSec experience in this article. When the attackers are within the enterprise network, they first need to figure out where they are. Once they accomplish this, they move towards their targets, and carry out the attack.

As we enter into 2018, what are some of the topics in AI/ML that are mostly hype? As we enter into 2018, what are some of the topics in AI/ML that are mostly hype? Let me break this down into a few categories. First, just a general note about how artificial intelligence (AI) and machine learning (ML) have been misapplied generally in the infosec market. While it's not incorrect to label what some infosec companies are doing as "Artificial Intelligence", it's certainly imprecise, and one can't help but wonder whether there's some latent hope to impress by sophistication.

As we enter into 2018, what are some of the topics in AI/ML that are mostly hype? As we enter into 2018, what are some of the topics in AI/ML that are mostly hype? Let me break this down into a few categories. First, just a general note about how artificial intelligence (AI) and machine learning (ML) have been misapplied generally in the infosec market. While it's not incorrect to label what some infosec companies are doing as "Artificial Intelligence", it's certainly imprecise, and one can't help but wonder whether there's some latent hope to impress by sophistication.

A majority of information security professionals believe that artificial intelligence (AI) technology will be used in attacks against them in the next 12 months, according to new research from Cylance. The security vendor polled Black Hat USA attendees last week to gauge their thoughts on the rapidly emerging technology. It revealed that 62% of them believe there's a high possibility that AI will soon be used offensively by hackers. However, this will only accelerate the use of the same tech for defensive purposes as organizations look for smarter ways and more efficient ways to stop increasingly automated attacks, claimed Cylance. The vendor provides AI-based advanced threat protection rather than more traditional detect and respond approaches, claiming it enables customers to switch on "pre-execution attack prevention".

While AI and machine learning are buzzwords, Symantec's Nick Savvides said, during this year's AusCERT conference they have been a big deal in computing circles since the 1950s. But it was in the 1980s when AI came into mainstream thinking a culture. It was movies like War Games and The Terminator, and TV shows like Knight Rider that took this important technology and moved it into mainstream consciousness. Savvides pointed to KITT, the automotive star of Knight Rider, as an example of what AI might one day deliver. "It had the ability to perceive, to provide constant analysis and make decisions," said Savvides.