More personal and organizational data is being shared, captured and stored online than ever before. This digital ‘treasure trove’ is enticing for cyber-criminals – it’s estimated that by 2023 cyber-criminals will steal an estimated 33 billion records.
Cue cybersecurity’s newest hero: the data scientist. There is a need for data-driven solutions to cybercrime. A recent report from Indeed showed a 29% increase in demand for data scientists year over year, and a 344% increase since 2013. This demand comes from the needs of cybersecurity, as well as data scientist’s multi-faceted skills across a wide range of industries such as:
- E-commerce
- Finance
- Healthcare
- Insurance
- Telecommunications
We’ve taken a look at what a data scientist does, and the links between data science and cybersecurity in our increasingly connected world.
The Busy Schedule of a Data Scientist
The task of data analysis is mostly handled by data scientists. They liaise with stakeholders to understand what information they need to look for, which in turn helps inform what algorithms and methods need to be used across their analytical tools. Data scientists will run well-planned data models, receiving the information needed for business growth.
They then present their findings in an easy to understand format by using data visualization techniques. This helps to go from a confusing spreadsheet to visually engaging charts and graphs, which better communicate the findings of data modelling. This work can help businesses gain insight into customer feedback, internal performance and product outcomes.
The entire process a data scientist undertakes remains consistent and successful by upholding security, integrity and privacy.
Security
With an estimated 12 billion records leaked in 2018, cybersecurity for data scientists is a high priority. Weaker security protocols can lead to vital business information being leaked or stolen, an expensive issue to have, with such cybersecurity breaches costing the world almost $600bn USD in 2018.
Cyber-attacks can pose a great danger to a business’ success, and the expertise of data scientists is sought after in order to prevent attacks from happening.
Integrity
Ensuring integrity across all modelling is a key part of a data scientist’s role. This involves validating your assumptions about data findings and making sure they match realistic outcomes that contribute to business success. Understanding where data is coming from and how it interacts with stakeholders is key.
Privacy
Preserving privacy and unbiased ethical standards in data science is critical for those working in the field. Ensuring that a user’s private data remains so is an important aspect of data ethics, treating such information with confidentiality and transparency. Data scientists can also ensure there is no bias present, and actively program machine learning algorithms to remain unbiased.
The Impact of Data Science in the Cybersecurity Industry
Data scientists use machine learning to identify potential cybersecurity threats, working to halt them. Machine learning automation makes identifying any outliers in data much easier. This allows data scientists to predict risks based on past exploits and behavior patterns. Their work is vital to maintaining cybersecurity, protecting businesses and the wider community from having their information stolen.
Cyber-attacks may initially appear quite minor, but machine learning can find patterns with minor outliers that could lead to larger threats. There is a constant battle between cyber-criminals and cybersecurity teams. Data scientists are challenged with staying ahead of threats, balancing predictive and reactive methods.
Fraudulent behavior is an area where data scientists can use machine learning to make a large difference across a number of industries. Regression (prediction) models are a great tool that use an Intrusion Detection System (IDS) to monitor computers for potential malicious attacks.
Associate Rule Learning (ARL) is another example of where machine learning can prevent cyber-attacks. This works as a recommendation system, similar to how Netflix or Spotify suggests new media for consumers based on their past preferences. ARL generates a response for a particular risk based on its characteristics. Past threats with the same characteristics will help ARL understand what may or may not be a threat, constantly updating its database with new types of cyber-attacks.
For those working in data science, it’s integral to understand the management, security, privacy and ethics that underpin data and information. One way to keep up with data science trends and technologies is to specialize in Data Science.
