Data Science in Data Security

Introduction

In the context of computing and Information technology in general, data science is driving massive shifts in the technology world and its general operation. Various techniques, processes, and scientific methods are employed o understand and analyze the actual phenomena with data. Due to the sudden increasing dependency on digitalization and the internet of things, various security incidents have grown exponentially and at alarming rates. It implies that hackers also want to manipulate the data for fun or gain financial profits when there is valuable data.  These security incidents like cybercrimes and attacks can cause disastrous financial losses and hugely affect organizations and individuals. Large enterprises are more focused on data-centric services and products with the digitalized economy. Therefore, these data-centric services and products should be designed with security in mind (Bhrugubanda. M., 2021).

Data science methods and techniques help solve some of the most disturbing problems in this field, including managing large amounts of data. It takes a look into a given data to ascertain its security, authenticity, and, most importantly, its originality. The ruse of technology has undoubtedly complicated the process of securing our systems.

Research Questions

  1. How does Data science help in data security?
  2. How do these Data Science techniques manifest in real-world applications?
  3. How do these techniques apply in Insurance companies?
  4. In 10 years to come, will we still be talking about data science? Or will there be a new form to ensure the safety of data?
  5. Technically, how do these data science tools enlighten the load for cyber security teams?
  6. What are the different techniques used in data science?
  7. Is data science the final answer?
  8. What does the future of data science hold concerning data security?

Research Questions discussion

How does Data Science help in Data Security?

Data science acts as a discipline that offers a modern scientific approach to identifying malicious attacks on the digital infrastructure. It mainly concentrates on applying insights and knowledge gained from data science to help defend computer systems from threats and attacks. Similarly, data science uses data-focused techniques and approaches that involve applying machine learning procedures to look for and identify unwanted threats and attacks.

This discipline helps to provide products like forecasts, detection, statistical analysis, and predictions. Firewalls and anomaly detection are how systems detect malicious attacks and determine the best course of action. Lately, data science has become a powerful tool in the ever-changing domain of data security (Gupta. R., 2021).

How do these Data Science techniques manifest in real-world applications?

The role of data science has not evolved in an overnight period. With different digitalization techniques, these data science systems serve various functions depending on the task at hand (Piccialli. F., 2021). Other applications have helped build the concept of data science security in today’s world, including fraud and risk detection, healthcare, Internet search, speech and advanced image recognition, gaming aspects. For instance, banking companies learned to apply the divide and conquer rule on data via past expenditures and customer profiling over the years on fraud and detection. This act enabled them to push their banking products and services based on their purchasing power (Hussaini, 2021).

How do these techniques apply in insurance companies?

Pertaining insurance mainly allows providers to reduce threat risks and enable a streamlined flow of their operations. Moreover, it has positively impacted insurance companies to get a more comprehensive range of information sources for the relevant risk assessment. Insurance companies are dependent on third-party marketing databases and social networking sites (Gupta. R., 2021).

In 10 years to come, will we still be talking about data science? Or will there be a new form to ensure the safety of data?

As significant advancements in technology keep coming, the accessibility of data science at a base level has increasingly become more and more democratized. As per research, both businesses and the environment they operate in will have evolved as we advance. There will be a thirst for data science maturity whereby different perceptions will reflect their expertise rather than how they are demonstrated (Tewari, 2021, pp. 63 – 79).

Technically, how do these data science tools enlighten the load for cyber security teams?

This aspect helps security operators to normalize the data sets and extract compromising indicators. Adopting advanced is an option for many security operators since security specialists rush to reduce manual workloads and the urge to flag malicious activities based on differences with known safe activities. These systems have proven to drive a favorable impact inside the security operations centers since they actively help identify attack patterns and increase the chances of detecting threats before time runs out. These data science techniques have enabled most organizations to ease their thirst for talent in the much-hyped and competitive data security by maximizing performance efficiency and lowering unnecessary workload (Sharma, 2021, pp. 33 – 63).

What are the different techniques used in data science?

Different kinds of analysis are available for different types of business enterprises to undertake in today’s world. The final projection of data science varies significantly with the type of data present, and hence the impact it creates is variable. The essential goal of this technique is to seek relevant data and detect weak links, which tend to make a model perform poorly. At times, the method employed is divided into supervised and unsupervised. Supervision implies that the target impact is well known, while unsupervised means that the target is not yet known and is still trying to achieve it. With a complex understanding that each analysis is vast, we provide a small amount of flavor to the different techniques used.

Usually, data represents the relationship between two or more variables, and its main focus is to plot some multidimensional plane that best portrays the relationship. By identifying these relationships, we give meaning to the otherwise randomness of the data, which can then be visualized and analyzed to provide specific information that organizations can use to make decisions or plan specific strategies (Baviskar, 2021).

Is data science the final answer?

Over the past few years, data science has enabled us to solve diverse and complex problems using machine language and statistic Algorithms. We are living in a data-driven world, with the current focus being data utilization. The scale of data analysis is genuinely outstanding, making consumers much aware of their data privacy rights and data habits (Echihabi, 2021, pp. 2369 – 2372).

What does the future of data science hold concerning data security?

While different businesses are integrating other data science techniques to ensure the safety of their data, they are often met with challenges and obstacles in finding the right solutions to help guide these initiatives. Data is a dominant theme today, and it is poised to play an influential role in the future. According to researchers, data will define business marketing and management; it would also help determine the modern health care systems, finance systems, and government.  The emergence of this discipline will give the business world a whole different meaning (kolaczyk, 2021).

 

 

 

 

 

 

 

 

 

 

References

Bhrugubanda, M., & Prasuna, A. V. L. A Review on Role of Cyber Security in Data Science.

Gupta, R., Saxena, D., & Singh, A. K. (2021). Data Security and Privacy in Cloud Computing: Concepts and Emerging Trends. arXiv preprint arXiv:2108.09508.

https://arxiv.org/abs/2108.09508

Piccialli, F., Bessis, N., Jeon, G., & Pu, C. (2021). Introduction to the Special Section on Data Science for Cyber-Physical Systems.

https://dl.acm.org/doi/fullHtml/10.1145/3464766

Hussaini, I., & Bashir, Y. A. A. B. EFFECT OF INFORMATION AND COMMUNICATION ON FRAUD PREVENTION AND DETECTION IN DEPOSIT MONEY BANKS IN NIGERIA.

Tewari, S. H. (2021). The necessity of Data Science for Enhanced Cybersecurity. International Journal of Data Science and Big Data Analytics, 1(1), 63-79.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3802495

Sharma, S. (2021). Role of Artificial Intelligence in Cyber Security and Security Framework. Artificial Intelligence and Data Mining Approach in Security Frameworks, 33-63.

https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119760429.ch3

Baviskar, M. R., Nagargoje, P. N., Deshmukh, P. A., & Baviskar, R. R. (2021). A Survey of Data Science Techniques and Available Tools. International Research Journal of Engineering and Technology (IRJET) e-ISSN, 2395-0056. https://irjet.com/archives/V8/i4/IRJET-V8I4816.pdf

Echihabi, K., Zoumpatianos, K., & Palpanas, T. (2021, April). High-Dimensional Similarity Search for Scalable Data Science. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) (pp. 2369-2372). IEEE.

https://ieeexplore.ieee.org/abstract/document/9458625/

Kolaczyk, E. D., Wright, H., & Yajima, M. (2021). Statistics Practicum: Placing’Practice’at the Center of Data Science Education. Harvard Data Science Review.

https://hdsr.mitpress.mit.edu/pub/twyc748y/release/1?readingCollection=3974b7e6

 

 

 

 

 

 

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