Big data and analytics are being used across a wide range of sectors, helping businesses to create new growth opportunities and make improvements to products, services and customer experience.
Now big data is showing promise within the cybersecurity sector. A 2016 report by MeriTalk identified that 90% of respondents surveyed in the US government had seen a decline in security breaches and 84% of respondents said they had used big data to help block such attacks.
The report, Navigating the Cybersecurity Equation, goes on to state that 49% of Federal agencies say cybersecurity compromises occur at least once a month as a result of an inability to fully analyse data.
What is big data?
Before we dive in, a quick recap on big data. Big data is a term used to describe the use of large volumes of data that are too heavy or complex for traditional processes. That data is handled, processed and then analysed by companies to keep track of trends and gain insights in order to improve aspects of a business such as operations.
Typically, the bigger the data, the bigger the insights. However, many businesses have struggled to fully understand the large volume of data and then analyse and interpret the data in a way that will improve business performance. You need to analyse the data correctly in order to make the best decisions for your business.
How is big data being used to analyse potential threats?
Whilst it may now be much easier to collect huge amounts of data, it is important to understand how such data can be used to improve cybersecurity practices. Companies can perform an in-depth analysis initially to establish data ‘norms’. These norms, coupled with future analyses, allow a company to quickly identify any deviations between the two to uncover breaches and attacks.
The next step is to automate as much of the process as possible so that deviations can be picked up and dealt with quickly by the appropriate people. This can be achieved by simplifying complex data structures into forms that can be worked with more easily. This process becomes even more effective over time as long-term trends pick up deviations that were not spotted in a one-off check.
The meteoric rise in big data is enabled by machine learning (ML) and deep learning (DL) which are subsets of artificial intelligence (AI). DL and ML techniques can process large amounts of data to identify patterns. These patterns enable businesses to identify potential cybersecurity threats.
The biggest issue faced by cybersecurity teams is the ability to analyse the data in a timely manner, extracting valuable insights and identifying threats quickly to enable incident response.
How big data is changing the cybersecurity sector
Before big data, the cybersecurity landscape looked a lot different. As cybercriminals have become more sophisticated in their attack methods, big data has become an essential tool in tackling new threats for businesses and, to some extent, individuals.
Prior to the use of big data, there were two main methods in the detection of cybersecurity threats:
- Risk assessment and identifying vulnerabilities – this is done by scanning an organisational network for known attack patterns or potential vulnerabilities where an attack could be directed. This is still an important aspect of cybersecurity – knowing potential areas of vulnerability is crucial, especially as part of a thorough incident response plan. However, big data automates such processes to cut down on manual scanning and monitoring.
- Correlation rules – cybersecurity experts define rules that specify event sequences that indicate anomalies that could be the result of cyber-attacks. This is still an important and integral part of cybersecurity, now automated through the use of big data and analytics.
Before the use of big data, there were some issues with the way cybersecurity experts were able to identify and respond to threats. At times, it was like looking for a needle in a haystack with cybersecurity specialists working on their ‘best guess’ while knowing potential vulnerabilities and monitoring those areas. Once threats started to originate from different areas and new, unique threats identified, cybersecurity teams were not equipped to deal with emerging threats such as zero-day attacks.
Another big issue before big data was the incident response time. Traditional methods of cybersecurity relied heavily on specific rules and correlations and these were simply not as effective at identifying threats automatically detected via the analysis of big data. By the time a threat was identified, it would be too late. This is primarily why big data is changing the cybersecurity sector for the good.
Big data cybersecurity solutions
Today, big data is helping businesses to improve their cybersecurity in a number of different ways including:
Intelligent risk management
We talked above about the importance of risk assessment in traditional cybersecurity and today, through the use of big data, the practice is no less important. Cybersecurity tools must be backed with intelligent risk-management insights that big data experts can interpret. This approach allows cybersecurity specialists to source, categorise and handle security threats without significant delay.
Big Data analytics help you foresee the class and intensity of cybersecurity threats. You can weigh the complexity of a possible attack by evaluating data sources and patterns. These tools also allow the use of current and historical data for the statistical understanding of which trends are acceptable and which are not.
Incident response is a type of approach for dealing with security with many kinds of security incidents including cyber-attacks and security breaches. To do this, Incident response focuses on detecting, identifying, isolating and eradicating the root cause of the incident. While incident response is not something new, big data has enabled many critical improvements to incident response and is regarded as an integral part of incident response programmes moving forward.
Intelligent big data analytics enables experts to build a predictive model that can issue an alert as soon as it sees an entry point for a cybersecurity attack. ML and AI play a major role in developing such a mechanism. Analytics-based solutions help you to predict and gear up for possible events.
Protecting your big data
The potential benefits of using big data analytics for cybersecurity are significant. However, they also come with a risk. The thing that makes big data analytics such a valuable tool in the fight against cybercrime, is also its biggest weakness.
In large organisations with hundreds of employees, big data systems collect and analyse huge amounts of data. In the wrong hands, that information becomes an absolute goldmine and has become a target for cybercriminals, usually through ransomware attacks.
Therefore, at the heart of any cybersecurity programme that incorporates the use of big data and big data analytics, you must always have a failsafe to ensure that the data you are collecting cannot be used against you.