As most are aware, Big Data Analytics can be incredibly insightful and valuable to any enterprise. What some are not aware of is every new data stream created is potentially a new attack vector for organizations to defend against. Historically, organizations have had to choose between security or analytics, but with the advent of strict data privacy and protection regulations, the choice has been removed out of the equation. So, with security now the priority, is it possible for businesses to get security right without data analytics suffering?
In this digital age, data is now the golden egg needed to further business operations and it has almost revolutionized the way enterprises conduct business in the 21st century. In fact, the Big Data Analytics market is set to reach $103 billion by 2023. Through Big Data analytics, companies have the ability to improve efficiency, services and products which ultimately benefits their customers, and this is where the return on investment is measured. By having the data in front of executives, they can gain insights and view patterns on human behavior to help make clearer business decisions. And, thanks to digital transformation, Big Data is everywhere, whether that be in the cloud, or streamed from devices.
However, with businesses harvesting, analyzing and transferring large data sets outside the standard business boundary in order to connect with other digital environments, an uncontrollable web of connectivity has transpired. With enterprises often having multiple partners, suppliers and other third parties, protecting this network for be extremely challenging, especially with no guarantees the third parties are protecting the critical information. Matters are made worse when considering hackers are becoming more ingenious with their attack methods, which has resulted in 78 percent of organizations experiencing a successful cyberattack in the last 12 months. It’s clear that data security can no longer be overlooked.
Why All-encompassing Security is Needed
The true value of Big Data cannot be measured, but the more detailed or sensitive the information is, the more value it has to a business. Unfortunately, cybercriminals are aware to this fact and, in the first six months of 2019, roughly 4.1 billion records have been exposed or stolen through successful cyberattacks. But who’s duty is it to ensure security is being met? Well, it’s a collaborative effort that must involve the entire enterprise from the C-level executives, to the data analysts and engineers, right down to the rest of the workforce. Almost every department in a company comes into contact with sensitive data, so it’s high time everyone understood the threats to data are very real… and costly if not adequately addressed.
The other major concern for businesses is ensuring compliance is being met against industry regulations and data privacy laws that require the protection of sensitive data. While data privacy statutory law is still in development at the federal level, many US states have filled the void and enacted their own data privacy laws – the strictest being the California Consumer Privacy Act (CCPA) – which are similar to the European General Data Protection Regulation (GDPR). In either case, if an organization is found to be non-compliant then serious fines ranging in the millions could be levied. If financial or credit card information is being harvested, then PCI DSS compliance is also required. These are numerous regulations that need to be adhered to depending on where the company operates or who the information relates to. So, to help navigate through the many data protection regulations, enterprises are seeking security solutions that address these concerns in order to future-proof their businesses.
The Data-centric Security Strategy
To tackle the complex nature of today’s online networks and help protect the data that lies within these infrastructures, a data-centric security strategy is required. This strategy is based on two key principles:
- Protecting the data at its earliest stage, which may seem obvious, but is often not routinely done by organizations. If sensitive data is secured from the offset – the moment it is collected – there is less risk that information is shared in its unprotected form.
- Only de-protecting data when absolutely necessary. If individuals or applications need to view a piece of protected sensitive data in plain text, then only do so when it’s essential. This links back to principle one, where data is always protected.
Traditionally, data in its rawest form was easier to analyze and process but doing this securely was often a challenge. Now, tokenization technology can allow for data processing and analysis in a protected manner while keeping overall operational impact to a minimum. Tokenization substitutes sensitive data with a non-sensitive equivalent (known as a token) and in doing so, data engineers can carry out analytics and gather insights while the data is still in its protected form. By defending the sensitive information at its core, and at every stage of its lifecycle, instead of simply building a wall around the data, it removes the main issue brought by legacy security solutions.
The need for Big Data will continue to grow as it becomes the key for enterprises to stay competitive in a dynamic market. By implementing a “data-centric security” solution, organizations can carry out Big Data analytics without having to compensate on security. Importantly, it will all be under the lens of compliance, to ensure that adherence with data protection regulations is being met.