Security is a broad term, and in industry and government there are a myriad of "security" contexts on a variety of levels – from the individual to nation-wide. Artificial intelligence and machine learning technologies are being applied and developed across this spectrum. While many of these technologies have the potential and have greatly benefited society (helping reduce credit card fraud, for example), the evolving social contexts and applications of these technologies often leave more questions than answers – in terms of rules, regulations and moral judgments – in their wake. Artificial intelligence and security were – in many ways – made for each other, and the modern approaches of machine learning seem to be arriving just in time to fill in the gaps of previous rule-based data security systems. The purpose of this article is to shed light on current trends and applications, in industry and government, at the intersection of artificial intelligence and the security field.
You say you want a revolution Well, you know We all want to change the world. The Big Data revolution's got all kinds of scientific terms buzzing around today's boardrooms at warp speed, smashing into each other on the path to becoming enterprise-wide solutions to business success. But what do they really mean? And more specifically, what do they really mean to marketers? You say you got a real solution Well, you know We'd all love to see the plan Before we start breaking each one down, let's first understand that they're all centered around one core principle: And knowledge is gained through information.
Artificial intelligence is a thing. No matter where you turn, technology companies are selling AI as the secret sauce in their cybersecurity platforms, their decision support systems, their network analytics tools, even their email marketing software. You name it, it's got "AI Inside." You'll see that acronym AI often, as companies refer to artificial intelligence that way – which in itself is pretty vague, as you'd expect for a term that's been bandied about for many decades and has a great number of representative branches. In our current context, AI generally refers to hardware or software that thinks, learns, and cognitively processes data the same way a human would, although presumably faster and more accurately: Think about Commander Data from Star Trek as a human-shaped role model for what AI could become someday.
For the last twenty years or so, the mental model of analytics maturity has been along the lines of the diagram below, starting with basic gathering of data and culminating in proactive, automated use of advanced algorithms. Gartner, for example, refers to four levels of capability: descriptive, diagnostic, predictive, and prescriptive analytics. Organizations are normally assumed to evolve from left to right as they become more mature in their use of analytics--but the reality is more nuanced. The horizontal axis shows the complexity of the analytics technology rather than time. While many organizations struggle to cross the "chasm" to predictive analytics because of limited data readiness and skills, it is possible to use a combination of the different technologies simultaneously for different needs and projects.
Last week saw a number of interesting events in Europe, including Big Data Spain in Madrid and GOTO in Berlin. Both had presence from key industry figures and organizations, were well-attended (in the range of 1000 participants give or take), well-organized, and forward-looking. The Internet of Things is creating serious new security risks. We examine the possibilities and the dangers. And both highlighted in a very real way the advent of AI in the lives of everyday technical and business people, reflected in the program of the events and the interest AI-related topics attracted.