Artificial Intelligence (AI) is seemingly everywhere…

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SwissCognitiveUnless you’re living under a rock, you’ve surely noticed the AI-hype-train ploughing through the media promising no less than transforming every industry. While there’s certainly currently both exaggerated claims and inflated expectations when it comes to Artificial Intelligence, more than the general public may think is already in the realm of reality rather than fiction. In fact, most likely you’ve been knowingly or unknowingly using AI-based technology already more than once today by, for example, either using Face ID to authenticate on your iPhone, searching on Google, reading subtitles on YouTube or looking at recommended items on Amazon. And, as with many other technologies, as soon as it works it’s not considered AI (technology) anymore.

AI is also all over cybersecurity. While 2-3 years ago only a few vendors positioned themselves on AI, nowadays every player in the market claims to have AI built in. Customers’ opinions about AI in security vary widely: while some believe that it will redefine cybersecurity and put an end to the cat-and-mouse game between attackers and defenders, there are also many skeptics who degrade everything related to AI as marketing-buzz.

What is the true potential of AI in security? What applications are already available now? What are the limitations? What questions should I ask my cybersecurity vendors? How are attackers using AI? Will AI take our jobs and kill us all? Let’s try to find answers to these questions.


AI vs. ML vs. DL – and why now?


The terms Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are sometimes used interchangeably, which is not quite correct. While AI is a very generic concept comprising all sort of intelligent agents (including expert systems with a lot of if/else statements in the code), ML systems are a subset of AI that learn from data. That is, ML algorithms are able to generalize and build models from examples without being explicitly programmed. Supervised learning (we have labeled data, e.g. “this e-mail is spam”), unsupervised learning (no labels) and reinforcement learning (agent acts in an environment and tries to optimize the rewards of his behaviour) are the most common classes of ML. In supervised learning classification (tell whether email is spam or ham) and regression (predicting a value) are the most common use cases. Artificial Neural Networks (ANN) are specific ML models with layers of connected neurons that are used in all of the three ML disciplines above. To put it simply, Deep Learning (DL) is subset of ANN where we have two or more hidden layers of neurons. All of the above are algorithms based on mathematics (and statistics) and not magic (even though some vendors make it sound otherwise). […]


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