The key theme of today’s day and age is “connected”. Whether it’s the Internet of Things (IoT), connected solutions and mobility, the world’s electronic devices are getting increasingly connected, which of course, the amount of information being collected as only getting more and more massive. This is the age of big data – organizations are now collecting huge amounts of data, thanks to the range of devices that are connected to the Internet. The International Data Corporation (IDC) has predicted double-digit growth for the worldwide big data and business analytics market to 2020. It is obvious that big data brings with it many advantages. But what about the disadvantages?
As can be understood from the name itself, big data is not like conventional data. The volumes are huge and with it, come a greater risk. The more data an organization stores, the more it needs to worry about how to secure it from the malicious threats out in the wild. Solutions need to be found which not only can keep up with the different varieties of the dataset, but also the speed at which it moves.
Big Data, bigger risks
It’s also been found that many conventional security solutions cannot always keep up with the multitudes of data that a company possesses. These solutions may have been designed to handle a certain amount of data but in this day and age, data is getting more and bigger and dynamic. When the data is continuously streaming, regular security checks are not just enough to detect patches. The outcome is also potentially more disastrous: Big Data breaches could also lead to greater damage and more repercussions.
Offering only a single level of protection may not be enough to handle the many risks posed by big data while as previously mentioned , the dynamic nature of Big Data makes it difficult for security solutions. In Big Data environments, data transfer is also mostly automated which brings its own risks and needs its own specific measures. The data also needs to be validated to ensure that it is accurate.
But apart from the infrastructural issues, cybersecurity experts will recognize that the key issue that plagues other breaches is also prevalent in this one: Human beings. Some of the major causes of breaches are user behaviour, whether deliberate or accidental. Access control plays a key role in this regard. Personal data can be gathered by information mining with users not being asked whether to provide data. Also many companies often do not have a proper access control structure in place with the result that key confidential data can be accessed by just about anyone in the company. The complexity of Big Data often means that it is not subjected to timely audits, hence decreasing the effectiveness of these audits.
Conversely, many organizations use Big Data techniques themselves to tackle security threats. Big Data analysis can help identify persistent threats, detect patterns and analyze the huge data for early identification of data threats. For example, the financial services giant Barclays implemented a big data solution to more than 44 billion security events each month.
Ideally, a proper big data security solution should provide continuous monitoring of sensitive data. By recording patterns of normal behaviour and data exchange, it can immediately flag any kind of suspicious transactions. The key factor here is real-time, which can capture breaches and unauthorized activities as it happens. The reporting should be automated which will accelerate incident response and investigations.
Hence, it is important for companies and organizations to employ a different perspective when it comes to dealing with Big Data. While Big Data is a boon with the amount of analytics and indicators it offers in a connected world, it can also become a big risk in the wrong hands. Having a proper security solution in place with a defined, structured Incident Response Plan can go a long way in making a company’s cybersecurity defense far stronger.
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