Small and start-up companies were an unlikely target for an advanced cyber-attack for a long time. A relatively unknown brand name and fewer monetary resources worked in their interest to ward off hackers. Today, the gate has been breached for small-midsize businesses and start-ups regarding cyber security.
The most attractive targets are companies that do more business online via cloud services that don’t use robust encryption technology. For cybercriminals, that means pilling up sensitive company information behind a weak door with an easy-to-pick lock.
As cyber-attacks grow in scale and complexity, infrastructure and traditional tools have become obsolete. The defense against the escalating number of cyber-attacks, big data solutions has come into play and shows their immense potential.
What is Big Data Analytics?
Big data analytics is the process of evaluating large and diverse data sets (big data) that are often underutilized in analytics programs and traditional business intelligence. The analyzed information includes semi-structured and unstructured data, social media content, web server logs, mobile phone records, and internet clickstream data. Customer emails, text from survey responses and machine data collected by sensors connected to the internet of things (loT) are also analyzed.
Big data analytics aims to find relevant data (such as market trends, consumer preferences, unknown correlations, and hidden patterns) that may assist a business in making informed decisions. Once done, it could allow a business to improve its operational efficiency, enhance its client service, undertake greater advertising strategies, find new sales opportunities, and gain a competitive benefit over its rivals.
The complex process of big data analysis generally involves complicated applications with factors, such as what-if analysis, statistical algorithms, and predictive models supported by robust computer systems, specialized software, and high-performance analytics systems. Big data analysts use analytic strategies, such as machine learning, data mining, statistics, natural language processing, and artificial learning. Big data analysts, engineers, and researchers generally use data analysis tools to access business data efficiently.
What is the Connection Between Big Data Analytics & Cyber Security?
Business data and information are some of the company’s most valuable assets. Entrepreneurs are increasingly attentive to the value of this data for company success in the present market economy. Top Companies provide Corporate Big Data Training to their High ranked employees to learn more about Big Data analytics.Now that mobile phones and wearable technologies are assisting businesses in collecting massive volumes of data from their users, privacy and security have become top concerns. This has resulted in entrepreneurs constantly looking for ways to keep their sensitive business information safe from the hands of hackers. This is where a big data analyst comes into play.
Businesses are using big data analytics to take care of the constantly evolving, sophisticated cyber threats resulting from the growing amounts of data being generated daily. The utilization of machine learning and big data analytics enables companies to perform a complete analysis of the collected information. The analysis reports provide indications of possible threats to the company’s integrity.
The equipment used by big data analysts works in real-time and generates security alerts according to their severity levels. Alerts are further enhanced with more forensic info for mitigation and instant detection of cyber breaches. Here’s how your company can use big data analytics to counter cyber security threats.
Analyzing Old Data
You can use historical information to predict upcoming attacks. Using big data analytics, your company can grow baselines depending on statistical information that tells what is expected and what is not. A detailed analysis like this can determine if there is a variation from the standard based on the collected data.
This risk assessment and a quantitative prediction of vulnerability to cyber attacks can assist your company in developing some counter-attack measures. In addition to helping you develop predictive models, analysis of historical data may also help you create AI-based algorithms and statistical models.
When big data analytics come in contact with machine learning, an entirely new possibility of cyber security opens. Because big data analytics collects, processes, and analyzes data based upon previous cyber-attacks, machine learning can enable you to develop individual defense responses. You can detect vulnerabilities in your network, find breaches in real-time, and tally information from more than one source by utilizing machine learning and big data analytics capabilities.
Monitoring and Automating Workflows
Studies show that many instances of cybersecurity threats are due to worker-associated breaches, also known as insider jobs, or can be put as backstabs. By using big data solutions, you can greatly lower the risk of these insider jobs, and this is because it plays a critical part in monitoring and automating workflows.
One way to mitigate these insider jobs is to restrict access to sensitive data to those employees who have the right to do so. Authorized personnel must use specific logins and system applications to view files and edit data.
These systems also allow data analysts to categorize and classify cybersecurity threats without lengthy delays, rendering the data irrelevant to the attack in front. By harnessing big data analytics capability, you can improve your cyber threat detection mechanisms and enhance your data management strategies.