GUEST ESSAY: How data science and cybersecurity will secure ‘digital transformation’

By Roger Huang

In today’s environment of rapid-fire technical innovation, data science and cybersecurity not only share much in common, it can be argued that they have an important symbiotic relationship.

A fundamental understanding of the distinctions – and similarities – of these two fields is good to have. Both must flourish separately and together to fuel “digital transformation” in a way that makes our connected world as  secure as it needs to be.

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Data science focuses more on data structures, algorithms and computability. Cybersecurity emphasizes knowledge of systems administration, architecture, operating systems and web applications. However, both data science and cybersecurity rely on proficiency across a shared base of technical knowledge.

Both disciplines also call on someone to properly assess the information before them, put it into the appropriate context and make proper decisions based upon it. Despite some differences between the two fields, there are many commonalities to explore.

Data science involves the gathering of data from multiple sources and sifting through it to analyze critical information and what it means for the future. This helps gain insights into how an entity (i.e., program, business, industry, trend and so on) is performing and how it can improve. The data also helps in forecasting how the entity will continue performing into the future or perhaps how it would do when faced with an obstacle (such as competition, a recession or other market or economic factors). The more data we have, the better insights we can reach and the better predictions we can make.

Two-sides, same coin

Huang

With data science becoming so prominent, cybersecurity is needed to protect the underlying data. Universities, huge corporations and governments depend upon the data they have to properly run and grow. Hackers can infiltrate computer systems and manipulate the data on them for nefarious purposes, such as identity theft, fraud, or in the most sophisticated instances, state-sponsored cyber warfare. You can almost see cybersecurity as the flip side of data science: as more and better data is needed to power predictive analytics, the responsibility of protecting that data from nefarious actors continually grows. In that sense, cybersecurity and data science are two sides of the same coin.

Furthermore, the fields of data science and cybersecurity overlap in another important way: both use machine learning to enhance their speed and efficiency.

Certain computer systems can have algorithms that allow them to learn without being explicitly programmed. This is what the foundation of artificial intelligence rests on. By automatically recognizing patterns within data, you can both glean better insights about the underlying data — and also recognize when it is being manipulated or attacked by a nefarious agent.

Data science techniques are often used to augment cybersecurity as well. Using techniques such as anomaly detection has allowed people who work in cybersecurity the ability to flag suspicious patterns that might correspond to attacks and allow them to zoom closer when human intervention is needed.

Knowing that many data science techniques are now used to detect attacks, a lot of research is being dedicated to concepts revolving around adversarial neural networks, in effect using artificial intelligence techniques to train one computer to fool another programmatically.

Intersecting technologies

Another area of overlap is the mentality required to be successful at either data science or cybersecurity. You need to be able to think at scale, and use technologies that can help pinpoint certain patterns with predictive power — in order to drive insights from data or to protect it from new and ever-changing cybersecurity threats.

In practice, this means that both fields will often rely on cutting-edge technologies, open-source libraries, and an ecosystem of sophisticated companies and startups providing services.

The technologies that both cybersecurity professionals and data scientists use are often intersected. Both will use Python as an essential programming language that can be used for scripting and automating tasks. For things like version control and essential system functions, both cybersecurity professionals and data scientists can use mainstays such as Git and Linux. Fundamental mathematical applications in both cybersecurity and machine learning can be implemented in the handy Python NumPy library and building web clients for scraping can also be handled in Python.

In summary, while cybersecurity and data science have their differences, the two do share some similarities, everything from the mentality to the technologies used.

(About the essayist: Roger Huang is the Growth Lead at Springboard, an online training company. Now based in San Francisco, the Canada native helps people get highly skilled digital jobs while enjoying salsa dancing and tofu poke bowls in his down time.

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