AI and Cybersecurity: Using Big Data to be safer

In combat, there are two main facets: defense and offense. That is no different in the cyber world. For-profits, and non-profits host information and other valuable assets with their ecosystems. They build out an environment that attempts to protect the assets. Unfortunately, we live in a world where individuals, groups, and sometimes nation-states try to expose a weakness in organizations’ defenses.

Every day millions/billions of transactions happen on the interact. With no need to meet in person, money is able to move more swiftly. Unfournantely, the condition also enhances the ability for fraud. As we continue to live our lives on/through the internet, it is going to become ever more prevalent that we feel safe.

Like many other fields, machine learning is paying a part in altering the space. Take an example, the promising startup Swift Science who uses machine learning to detect possible fraud threats. Bussiness have found its service fruitful as it helps protect against financial loss while also enhancing the user experience.

One way to improve the security of interactions is by increasing the number of borders that need to be broken. Consider, the normal more common safety measure: multi-stage verification. However, if too many boarders are added the user experiences begins to suffer, A rules-based approach is too stringent. The number of scenarios quickly scales. This is where machine learning can come in. Instead of being hindered by the vastness of the scenarios, it actually has a tendency to improve when provided with more information.

Take for example Swift Science. What it does is keep track of the behavior on company websites and builds out models that try to predict fraudulent behavior. The difference that Swift Science provides is that instead of providing the business with a model that was trained on behavior from analyzing other business traffic, the models are focused on a particular business. The main rationale behind the business model is that users and their behavior is particular to a specific business. How one behaves on Amazon might be different from how behaves on eBay.

Swift Science is just one of the many companies in the security space that are using artificial intelligence. What promising AI cyber companies can provide include live adaptation, user control, and analytic ability.

Although it needs to be clear that artificial intelligence can/is a powerful tool for making the internet a safer space, it does not make cybersecurity a solved problem. The predictive techniques while powerful are not oracles. Like in other domains making use of machine learning, the security is bolstered with additional information. There lies one of the critical difficulties. The machine learning models need to closely reflect the current state of the threat. So, is machine learning current and a future staple of cybersecurity? No doubt, but unsurprisingly the implementation of the machine learning efforts is paramount for success.



2 comments on “AI and Cybersecurity: Using Big Data to be safer”

  1. Interesting post!

    I think you’re right insofar as stating that AI makes cyberspace safer but does not solve the problem of securing cyberspace completely. However, companies such as Swift Science are definitely moving towards a more secure environment by adopting AI. Apart from Swift Space, Magnifier – a behavioral analytics solution – introduced by Palo Alto Networks uses machine learning to improve threat detection [1]. Many other companies such as Alphabet employ AI technologies like Chronicle to increase cybersecurity and analyze threat too.

    All in all, I think with the advancements in the applications of AI, in the near future – it can be predicted that AI would be a cybersecurity game- changer.


    [1] –

    1. The cases that you mention are definitely interesting. And, yes, there are many companies that will be using artificial intelligence with the next generation of cybersecurity. Needless to say, AI by a construct still lives on seeing examples to adapt. Without a system that is great at real-time (or as close as possible) we will continue to face similar problems.


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