Cyber Security plus Data Science: The Essential Career Path of the Future?

Datascience and cybersecurity, two of the most popular career paths, are on a collision course. The combination of these two skill sets have become highly sought-after in this age of technological advancements. There is a huge talent crunch when it comes to finding IT professionals. If you ask anyone in the IT sector‘whatjob positions are the most difficult to fill?’ you will almost certainly hear "data science" and "cybersecurity." Employees with these skills are hard to find and even harder to retain. What happens when the next generation of jobs will require a combination of these skills?

Many cybersecurity tool providers have been in a frenzy adding data science capabilities to their cybersecurity platforms. This includes factoring behaviour-based analytics and responses into antivirus, firewalls, and traffic analyzers to make their products smart. 

Artificial intelligence and data science can enhance traditional cyber security. However it’s a slow process to use cyber security in data sciences and artificial intelligence. So the next challenge becomes how to secure the black-box algorithms -- products of data science programs -- that learn and grow dynamically.Because these analytics models are so highly valuable to enterprises, cyber security professionals will need to determine standards and methods for protecting these models and ensuring their integrity. To do so, they will need to protect these assets from the outside in and the inside out.

Just as cybersecurity professionals are trained and skilled at managing the limit of the network, they must develop controls around the limit of the black-box algorithms that autonomously make business decisions.This includes two areas of focus.

First, they need to protect the data being fed into the model. The evaluation and assessment of the "goodness" of the data being input into the model is one front that a cyber-security professional can protect from the outside in.Second, they need to protect the model itself. Data scientists are often more like scientists than they are software engineers. Their focus is on research and development and creating new, exciting algorithms and models that can have major business impact. Knowing where a model came from and that it has not been maliciously altered will become the crusade of cybersecurity professionals worldwide as they protect the limit.

Models labelled as AI are, by nature, learning algorithms and are bound in a certain degree of uncertainty. Often, the model does not generate a definitive answer but rather a statistically probable answer. The challenge is that as behaviour evolves over time, the performance of these models will also change. Unlike traditional software engineering that can incorporate unit tests that pass or fail based on expected outputs given a set of inputs, measuring an artificial intelligence model can prove to be more troubling. However, it is still an important area for cyber security professionals to cover as they strive to protect the business.

One of the fundamentals of data science is the need to monitor a model's performance once deployed into production. Traditionally this has been a point of operational effectiveness so the data science team knows when the behaviour has evolved past initial constraints and needs updating to accurately represent new business conditions. Cyber security professionals can catch trends in the model's output and performance and thus detect and prevent danger to the enterprise.

Another mechanism to protect these models from the inside out is to establish results thresholds. Regardless of the model outcome, if these thresholds are exceeded, the transaction can be put into a holding area until it can be reviewed for legitimacy. An example of this could be limits on stock trades associated with algorithmic trading. The model itself is generating decisions about when to trade, when to sell, and at what price. Ceiling or floor price thresholds at which price a stock is sold would prevent hackers from tampering with the model constraints in order to manipulate the stock market.

The convergence of cyber security and artificial intelligence is dignified to be one of the hottest areas of IT growth in the coming years. With a talent crunch today for both cyber security and data science professionals, be aware that this up-and-coming job role of artificial intelligence cyber security is on the near-term horizon.