The application of data science is practically unbound.
No matter how complex or intricate a concern is, there is always a solution that can be framed with the help of data science and analytics.
The case is the same, even with cybersecurity.
Over the last few years, data science has grown to become a field that can support and enhance cybersecurity in a variety of ways than it only being something that needs to be continuously protected by security infrastructures and systems in place.
This has undoubtedly enabled data science to be responsible for significant changes happening in the field of cybersecurity. As machine learning and AI are changing the game of threat identification and countermeasure planning, the role of data science insecurity is only going to get more prominent.
As more and more businesses have come to use data as the most critical fuel that uplifts all their operations, it is no wonder why data science is seen as the next big thing in cybersecurity.
But, what is the role of data science in the broader context of cyber threats and security concerns?
Let’s find out.
Table of Contents
Data Science and Cyber Security Have a Close Relationship with Each Other
Businesses have access to some of the most influential and robust data analytics and machine learning tools in the market.
There’s no doubt about that.
Using these tools and techniques, organizations can assess the means and methods utilized to gather different types of data.
A series of assessments with the intent of finding patterns of glitches and threats can help a business certainly develop actionable insights for cyber threat planning.
For example, data analysis might help you understand that cyber threats happen from a particular network terminal. The patterns and instances of such risks can only be understood through practical data analytics, which needs data and robust analytical tools.
Also Read: 5 Encryption Algorithms For Cloud Data Security.
Threat Detection Systems and Methods of an Organization
Though security threats are common to all kinds and sizes of businesses, there is no doubt that such risks are more frequent in banking and insurance companies.
These companies will have not only boatloads of financial data but also sensitive healthcare data of the customers, which makes them robust for cybercriminals.
It is often said that cybersecurity is, most of the time, a game of trial and error. Cybercriminals use a crop of systems and techniques to gain access to steal data and other information.
This is exactly where various threat detection systems can come into play.
Such detection systems will carry out an extensive assessment of various machines, systems, infrastructure, and networks for flagging if any suspicious activity is detected.
Further, when present and historical data collected over some time can be rendered through various data analytics models, it can surely help a business understand where the threats are coming from and where they are successful. This will help them focus more on weak areas, all the upping the ante of the already well-performing areas.
Also Read: What Are Honeypots? Definition and Security.
Protecting Information and Corporate Interests
Corporate espionage is a serious concern that bogs companies considerably. There is no doubt that. A competitor that knows about the decisions that a business is taking and plans it is going to execute is a real challenge for any business.
It is observed in a report by IBM that it takes an average of 279 days to detect and counteract a data breach.
That’s steep, right?
Hence, data protection is a serious concern, no matter what way one looks at it.
No one knows where security threats and challenges can come from. If you have a small scale organization that has limited infrastructural settings to operate a business, an intelligent system regulating the usage of software and firmware programs can help them.
There is no doubt that such threats can challenge even large scale organizations.
However, effectively using the data sets available and the insights developed from the same, businesses can create highly robust security measures. For example, for data security and software authenticity, you can take benefit of the possibilities of code signing certificates.
The Inclination of Data to Facts against Assumption
Data analytics has historical proof to support decisions of business—regarding data security, consumer behavior, or market sentiment.
For more than a decade, the cybersecurity sector has always been searching in the dark for something they had no idea.
Varonis stated in its 2019 Data Risk Report that more than 53.00% of companies found all of its employees having access to highly sensitive data.
That’s more than half? What are we talking about?
Thanks to data science, it has changed in the recent past significantly.
The unique nature of data science to lean onto historical data and insights to move into the future based on facts is what makes data science a markedly important ally of the world in the fight against cyber threats looming above all of us.
In today’s world, where technology is ubiquitous, cyber threat is as real as it gets. However, with highly value-adding and extensive systems, logarithms, and predictive analytical tools, data science can make a real difference to the situation.
In recent years, data science has transformed cybersecurity in more ways than one.
With historical data and incidental analysis to learn from and make adequate infrastructure to thwart security threats, data science is undoubtedly leading the way of revolutionizing the fight against cyber threats and security concerns.