What is Big Data Analytics' Connection to Cyber Security?

Business information and data are among the most valuable assets any business has. In the current consumer environment, entrepreneurs are becoming increasingly aware of the value of this data for business performance. Now that mobile apps and wearable technologies help companies gather large quantities of data about their customers, protection and privacy have now become top concerns. This has kept businesspeople constantly looking for ways to keep their sensitive business data from the reach of hackers. It's here that big data analytics come in.

Big data analytics are being used by companies to contend with the ever-evolving, sophisticated cyber threats that stem from the increased volumes of data generated daily. The use of big data analytics and machine learning enables businesses to perform a thorough analysis of the collected information. The results of the analysis will in due course provide hints of any potential threats to the integrity of the business.

The tools used for analysing big data operate in real time and produce safety alerts according to their level of severity. More forensic details for quick detection and mitigation of cyber breaches further expand the alerts. Here's how the Big Data Analytics company will tackle cyber security threats:

I. Analysing historical information

Historical data can be used to forecast imminent attacks. By using big data analytics, your business can develop baselines based on statistical information which reveals what is normal and what is not normal. With such a thorough analysis, using the data collected, you can know when there's a variation from the norm.

Along with a quantitative prediction of susceptibility to cyber-attacks, this risk assessment can help your business develop counter-attack measures. Historical data analysis can also help you create statistical models and AI-based algorithms, in addition to helping you develop predictive models.

When big data analytics meets machine learning, cyber security opens a whole new possibility. As Big Data Analytics gathers, analyses and processes data based on previous cyber-attacks, machine learning can allow individual defence responses to be created. Big data analytics and machine learning technologies can be used to identify vulnerabilities inside the network, discover real-time breaches, and compare multi-source knowledge.

II. Tracking and automating workflows

Studies show that many cases of cyber security threats result from breaches related to employees, also known as inside jobs. With the use of big data analytics, the risk of these insider threats can be reduced considerably. This is because it plays a crucial role in monitoring workflows and automating them.

One way to mitigate insider threats is to restrict access to sensitive information only to those employees authorized to access that information. Authorized personnel will be required to view files and change data using specific logins and other system applications. The system administrator should work with the human resource department to provide different login details for authorized staff depending on their responsibility to the business and the level of complexity in their respective job descriptions.

Big data analysis plays an enormous role in mitigating violations in information security caused by employees in the company. It helps by facilitating the timely and efficient submission to a managed security service for further analysis of any suspicious events. The automation part of it helps the device to respond quickly to detected threats such as malware attacks.

III. Deploying program for intrusion detection

Big data analytics may uncover irregularities and suspicious activities in conjunction with network flows, logs, and system events. Since cyber breaches are becoming increasingly sophisticated, intrusion detection systems such as NIDS (network-based intrusion detection systems) are highly recommended as they are far more effective in detecting cyber security threats.

Cyber security is largely dependent upon the risk management and actionable intelligence provided by the analysis of big data. While it is essential to have tools capable of analysing data, it is not enough if those tools cannot automate tasks and relay crucial data quickly to the right people.

Today, there are many information securities tools available to help companies determine the threats they face. Another such structure is the framework of the National Institute of Standards and Technology (NIST). It provides an information security policy structure outlining how organizations should assess and improve their ability to track, deter and deal with cyber-attacks.

IV. Big Data Analysis to protect your company

So much as big data is vital to your business ' success, if it's improperly collected and analysed, it can be useless for threat analysis. Big data analytics solutions, backed by artificial intelligence and machine learning, give businesses hope that they can keep their data processes safe in the face of a hacking or cyber security breach.

Such systems often help data analysts to identify and categorize cyber-security threats without the long delays that might make the data insignificant to the attack. By using the power of big data analytics, you can improve your mechanisms for cyber threat detection and improve your data management techniques.

Examples of Data Science Uses

Additionally, here are few examples of how businesses are using data science to Novel in their sectors, create new products and make the world around them even more well-structured and Organized.

⦿ Healthcare Data Analytics

Data science has acquainted number of Development in the healthcare industry. With a broad network of data now available via all from EMRs to clinical databases to personal fitness trackers, medical professionals are finding new methods to understand disease, practice preventive medicine, diagnose diseases faster and search new treatment possibilities.

⦿ Self-Driving Cars and Data Mining

Tesla, Ford and Volkswagen are all executing portending analytics in their new wave of autonomous vehicles. These cars use thousands of mini cameras and sensors to reinforce real-time information. Using machine learning, predictive analytics and data science, self-driving cars can adjust to speed limits, avoid menacing lane changes and even take passengers on the fastest route.

⦿ Logistics Data

UPS turns to data science to escalate efficiency, both internally and along its carriage routes. There is tool which uses data science-backed statistical modelling and algorithms that create ideal routes for transportation drivers. The algorithm considers weather, traffic, construction, vehicular movement etc to suggest the best route. It is estimated that data science is economizing the logistics company up to 39 million gallons of fuel and more than 100 million delivery miles every year.

⦿ Entertainment

Do you ever amaze how Spotify just seems to suggest that perfect song you're in the mood for? Or how Netflix knows just what shows you’ll love to watch? Using data science, the music streaming can cautiously curate lists of songs based off the music genre or band you’re currently into. Netflix’s data assembler will recognize your need for culinary inspiration and recommend relevant shows from its huge collection.

⦿ Finance

Machine learning and data science have saved the financial industry millions of dollars, and quantitative amounts of time. Thanks to data science, what would take around 360,000 manual labor hours to complete is now finished in a few hours. Additionally, fintech companies like Stripe and Paypal are investing extensively in data science to generate machine learning tools that quickly detect and prevent fraudulent activities.

⦿ Cybersecurity

Data science is functional in each industry, but it may be the most influential in cyber security. Being able to detect and learn new methods of cybercrime, through data science, is essential to our safety and security in the future.