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For many years, small businesses were an unlikely target for a sophisticated cyber-attack. A relatively unknown brand and fewer financial resources worked in their favor to fend off hackers. Not anymore. Today, the dam has broken for start-ups and small-midsize companies when it comes to cybersecurity.
The most enticing targets are those businesses doing more business online through cloud services that don’t utilize robust encryption technology. To cyber criminals, that translates into heaps of sensitive company data behind a flimsy door with an easily picked lock.
With the increase in cyber-attacks in terms of volume and complexity, traditional tools and infrastructure have since become redundant. To tackle the escalating number of cyber-attacks, big data analytics has come into play and is showing its immense potential.
What is Big Data Analytics?
Big data analytics is essentially the process of assessing large and varied data sets (big data) that is often left unexploited by traditional business intelligence and analytics programs. The information that is evaluated includes a mix of unstructured and semi-structured data, for instance, social media content, mobile phone records, web server logs, and internet clickstream data. Also analyzed includes text from survey responses, customer emails, and machine data captured by sensors connected to the Internet of Things (IoT).
The aim of using big data analytics is to discover relevant information (such as consumer preferences, market trends, hidden patterns, and unknown correlations) that can help a business make informed decisions. Once done, it can enable a company to enhance its operational efficiency, improve its customer service, adopt more effective marketing strategies, find new revenue opportunities, and gain a competitive advantage over its rivals. Companies are even encouraging their employees to learn about data analysis management to improve their skills on how to make better strategies based on the data given.
The intricate process of big data analytics usually involves complex applications with elements such as statistical algorithms, what-if analysis, and predictive models driven by high-powered computing systems, high-performance analytics systems, and specialized software. Big data analytics makes use of analytic techniques such as data mining, machine learning, artificial learning, statistics, and natural language processing. Data analytics tools are usually utilized by analysts, researchers, and engineers for companies to access business data efficiently.
What’s the Relation of Big Data Analytics to Cyber Security?
Business data and information are among the most valuable assets that any business has. Entrepreneurs are increasingly aware of the importance of this data for business success in the current market economy. Now that mobile devices and wearable technology are helping businesses collect vast volumes of data about their users, security and privacy have indeed become primary concerns. This has kept entrepreneurs on the constant lookout for ways to keep their sensitive business data from hackers’ reach. This is where big data analytics come in.
Companies are using big data analytics to contend with the continuously evolving, sophisticated cyber threats rising from the increased volumes of data generated daily. The use of big data analytics and machine learning allows business to perform a thorough analysis of the information collected. In due course, the results of the analysis give hints of any potential threats to the integrity of the business.
The tools used for big data analysis operate in real time and produce security alerts as per their severity level. The alerts are further expanded with more forensic details for fast detection and mitigation of cyber breaches. Here’s how your business can utilize big data analytics to address cybersecurity threats:
Analyzing historical data
You can use historical data to predict imminent attacks. By using big data analytics, your business can develop baselines based on statistical information that brings to light what is and what’s not normal. With such a thorough analysis, you can know when there is a variation from the norm using the data collected.
This risk assessment together with a quantitative prediction of susceptibility to cyber-attacks can help your business come up with counter-attack measures. Besides helping develop predictive models, analyzing historical data can also help you create statistical models and AI-based algorithms.
When big data analytics meets machine learning, a whole new possibility for cybersecurity opens. As big data analytics collects, analyzes and processes data based on previous cyber-attacks, machine learning can allow you to develop individual defense responses. You can use the capabilities of big data analytics and machine learning to detect vulnerabilities within your network, discover breaches in real time, and correlate information from multiple sources.
Monitoring and automating workflows
Studies show that many cases of cybersecurity threats are as a result of employee related breaches, also known as inside jobs. With the use of big data analytics, you can significantly reduce the risk of these insider threats. This is because it plays a crucial role in monitoring and automating workflows.
One way of mitigating insider threats is to limit access to sensitive information only to those employees that are authorized to access it. Authorized staff will be required to use specific logins and other system applications to view files and change data. The system administrator should work with the human resource department to supply authorized staff with different login details depending on their responsibility to the business and the level of complexity in their respective job descriptions.
Big data analytics plays a huge role in mitigating cybersecurity breaches caused by business employees. It helps by facilitating the timely and efficient submission of any suspicious events to a managed security service for additional analysis. The automation aspect of it enables the system to respond to detected threats, such as malware attacks swiftly.
Deploying an intrusion detection system
Big data analytics, in conjunction with network flows, logs, and system events, can discover irregularities and suspicious activities. Given the growing sophistication of cyber breaches, intrusion detection systems such as NIDS (network-based intrusion detection systems) are highly recommended as they are much more powerful when it comes to detecting cybersecurity threats.
Cybersecurity largely depends on the risk management and actionable intelligence that is provided for by big data analysis. While it is essential to have tools that can analyze data, it is not enough if those tools cannot automate tasks and quickly relay crucial data to the right people.
Several cybersecurity frameworks are available today to help businesses to evaluate the risks they face. One of these frameworks is the National Institute of Standards and Technology (NIST) Framework. It presents a policy framework of computer security guidance of how businesses can evaluate and boost their ability to detect, prevent, and deal with cyber-attacks.
Securing Your Business with Big Data Analysis
As much as big data is crucial to the success of your business, it can be ineffective for threat analysis if it is poorly mined and processed. Big data analytics solutions, backed by artificial intelligence and machine learning, give hope to businesses that their data processes can be kept secure in the face of a hacking or cybersecurity breach.
These systems also enable data analysts to classify and categorize cybersecurity threats without the long delays that could render the data irrelevant to the attack at hand. By employing the power of big data analytics, you can enhance your cyber threat-detection mechanisms and improve your data management techniques.
Ken Lynch is an enterprise software startup veteran, who has always been fascinated about what drives workers to work and how to make work more engaging. Ken founded Reciprocity to pursue just that. He has propelled Reciprocity’s success with this mission-based goal of engaging employees with the governance, risk, and compliance goals of their company in order to create more socially minded corporate citizens. Ken earned his BS in Computer Science and Electrical Engineering from MIT. Learn more at ReciprocityLabs.com.