What Every Data Scientist Should Know About Cybersecurity

In the past, the collection of information and data was relegated to physical storage systems and repositories which could be protected through traditional security protocols. This included storing documents under lock and key or creating complex systems which were only accessible to specific individuals. These physical and material repositories of information and data have since migrated into the online realm and cloud-based computing systems. With the increased collection of data on internet users and consumers, more companies and institutions are being tasked with maintaining the security of private and personally identifiable information and data.

Many times this information and data is vulnerable to cyberattacks due to the nature of computational systems and sharing data within the era of information. Cybersecurity is focused on creating solutions to these 21st-century concerns. Through strategic planning and system development, cybersecurity professionals help protect stores of information and data online. Data scientists who are working with online data collections and/or sensitive data should learn about cybersecurity and its importance to the process of database management and design.

What is Cybersecurity?

Cybersecurity is an industry, field, and practice focused on the protection and defense of information and data. Primarily, cybersecurity includes the management and creation of databases, as well as systems for collecting, storing, and sharing information and data in a way that is safe and secure. Due to the increased mobility of data from one place to another, as well as the storage of data within centralized databases, information can be more vulnerable to attack from outside sources. Whether this is financial data that is vulnerable to attacks from hackers looking to steal credit card information or bank account numbers or bad actors that want to expose data or personal information about an individual or institution, there are many spaces that require assistance from cybersecurity professionals.

Within the field of cybersecurity, professionals work towards developing both offensive and defensive strategies which can protect information and data. Offensive strategies focus on the prevention of attacks before they happen, whereas defensive strategies focus on combating attacks while they are happening. This can include the construction of databases which include protocols around data accessibility as well as audits that can detect unusual behavior within a system, or even layers of encryption and password protection to access certain types of information. Cybersecurity professionals are also trained in methods such as creating firewalls and building database management systems that are less vulnerable to cyber-attack and ensure the protection of information and data during the process of collection and storage in the data science life cycle.

Why is Cybersecurity Important to Data Science?

Although data science and cybersecurity are separate fields, there is overlap between the two when it comes to the study of big data and user data in particular. Data scientists that work with databases that collect large stores of data on users or consumers also have access to data that can be described as sensitive, or personally identifiable information, like profile passwords and geographic locations. The burgeoning field of Cybersecurity Data Science (CSDS) takes a meta approach to the study of information and data. Within CSDS, data scientists are able to use data from cybersecurity analysts and systems in order to create better solutions for preserving the safety and security of information systems and technology.

Predictive modeling and the use of algorithms and machine learning is another area within the realm of data science that has been put to use in the service of cybersecurity. Through the auditing of data within a database management system, cybersecurity professionals can collect and analyze the ways in which users and data scientists engage with that system. This information can then be used to determine whether engagement with a system is unusual in some way. Within the realm of financial technology, we commonly see these database auditing algorithms employed to determine whether or not a hacker is trying to break into a financial system, or for fraud detection on an individual's account.

With all of these uses for cybersecurity within data science, more careers and employment opportunities are combining the specialties of data science and cybersecurity. Positions and roles as a Cybersecurity Analyst, Data Infrastructure Engineer, and Security Data Scientist are all examples of jobs that require advanced knowledge in the strategies of both cybersecurity and data science. In addition, there are several cybersecurity roles that can be viewed as adjacent to data science, such as database administration and management, which prioritizes constructing and overseeing secure data collection and storage systems.

The Future of Cybersecurity in Data Science

As the field of data science moves forward, the design and management of more secure databases are primary concerns of the 21st century. Careers that combine data science and cybersecurity are not only a reflection of changes in the industry but also updates to the software and tools that data scientists use. For example, many database management systems include algorithms that are sensitive to data breaches, malware, and other forms of cyberattack. Database management systems such as IBM Watson utilize artificial intelligence to strengthen the cybersecurity protocols of a database. Data scientists who work with these systems will also be able to further develop their skills and understanding of how to protect data through working with these new database management systems and storage protocols.

Interested in learning more about Cybersecurity?

The widespread collection and sharing of information and data online have made cybersecurity a fast-growing industry within and outside of the realm of data science. Noble Desktop offers data science classes that can expand your skills working with secure databases and protecting data from the process of data collection, to the stages of analysis and sharing. The Cybersecurity Bootcamp includes instruction on everything that you need to know in order to start a career in cybersecurity, such as offensive and defensive strategies for protecting information and data, as well as analyzing security threats.

>When developing skills in cybersecurity, it is also important to learn more about database management, and Noble Desktop’s SQL courses are based on some of the most popular relational databases within the data science industry. Through taking part in bootcamps and certificate programs that focus on SQL or NoSQL database management systems, data science students and professionals can build their knowledge of secure databases and the protocols of storing and managing information and data. Data scientists who are interested in expanding their career can think about pursuing opportunities that not only combine cybersecurity and data science but also include specialization in database management and design.