5 Ways for Enterprises to Implement Big Data Data-Centric Security Measures
Wed, April 21, 2021

5 Ways for Enterprises to Implement Big Data Data-Centric Security Measures

Data-centric security refers to an attempt to alter or disguise data “as a means of protection from misuse and prying eyes.” / Photo by: everythingpossible via 123rf

 

“Data is money,” said Anna Russel of tech online publication TechRadar. For some businesses, data is the highest commodity, meaning consumers now hold the power. By aggregating large pools of data, organizations can analyze human behavior and interactions through market patterns, trends, associations. The insights they will draw from the data will allow businesses to make decisions that tailor to their customers. 

To Russel, “big data is big business.” Hence, companies are investing in data science and analytical platforms. When you hear the word “data,” security should be the first thing that comes into your mind. Let’s consider the fact that data is present everywhere from the cloud to streaming, from sensors to videos. Unfortunately, the security aspect of big data is often overlooked. 

Data Breaches

Data breaches “have seemingly run parallel with the amount of data” firms are demanding in the last five years. Take Facebook, Yahoo, Dropbox, Equifax, Twitter, and Google as case examples. These high-profile companies are known to collect volumes of data but they are also the ones who are targets of data breach. The majority of data gathered by these firms is classified as “sensitive personal information.” Given that, cybercriminals are more determined to extract them to use for malicious purposes. 

Safeguarding personal information entails data-centric security. Data-centric security refers to an attempt to alter or disguise data “as a means of protection from misuse and prying eyes” rather than resorting to traditional routes involving IT systems or security of systems. Apparently, choosing the right data-centric solution can be tricky. Vendors can state they offer a data-centric solution but such solutions often fail to meet the demands of using it in big data analytics environment. 

Ideally, a data-centric solution requires a number of important aspects to cater to the needs of tomorrow’s analytical workloads. 

Data Lakes as a Panacea to Big Data Problems

Traditionally, service providers store data in a data warehouse known as a single repository to create reports, analyze data, and consolidate, wrote Mohiuddin Ahmed of news platform The Conversation. But data in a warehouse needs to be pre-processed, and it’s no easy feat since it costs time and money. 

Hence, data lakes are deemed as a panacea. Data lakes can store raw data, unlike warehouses. Sure, data lakes can create opportunities for digital crunchers, but it’s still unguarded, making it vulnerable to cyberattacks. Addressing cybersecurity issues “remain an afterthought,” Ahmed argued. An enterprise’s ability to extract and interpret intelligence from lakes is threatened in cyberspace, which is evident through the number of breaches and cyberattacks occurring all over the globe. 

Since malicious programs are becoming more advanced, it’s easy for hackers to conceal a virus within a file. It happens when a hacker takes advantage of freely available tools to compromise a system connected to the internet, injecting it with false data. The data then gains unauthorized access to the data lake, manipulating its contents to mislead users. 

 

Traditionally, service providers store data in a data warehouse known as a single repository to create reports, analyze data, and consolidate. / Photo by: bluebay via 123rf

 

How Businesses Secure Their Big Data Environments

Here are some characteristics of the more effective security measures.

1. Scalability and Speed

A data-centric solution needs to incorporate scalability, as big data environments will be in constant use. Hence, it is a must for businesses to have housing security that can keep pace. A data-centric solution should have the ability to scale any workload, whether it’s in real-time or for historical use. This should not in any way impact or hinder performance.  

On the other hand, businesses are starting to venture into machine learning and AI. Companies can leverage these technologies by incorporating them into their systems, allowing firms to process data with less manpower. In this case, speed is the key. Therefore, security needs to stay abreast of the business itself. To achieve this, organizations must find a data-centric security solution that enables it to execute its intelligent features “for both streaming and load distribution.” 

2. Availability and Adaptability

Security should never hinder availability and adaptability. If issues arise, security technology needs built-in fault tolerance capabilities to ensure that these are addressed immediately and without interruption. Of course, organizations must have access to data regardless of where it is available to guarantee maximum value and protection. 

3. Flexibility 

Businesses employ big data frameworks like Spark, MapReduce, and Hadoop to undertake big data projects. However, these frameworks operate on legacy systems. Since new technologies continuously pop up, a business’s data-centric solution needs to have a degree of flexibility in case it becomes outdated. 

4. All Environments Covered

A data-centric solution should support the company’s current and future big data analytics environment regardless if it’s on-premise, in the cloud, or both. To minimize needless expenditure, enterprises should aim for a platform that can handle native and API-based integration. 

5. Tokenization

The best way for enterprises to safeguard data and enable analytics is through tokenization. Tokenization is defined as substituting data with a “non-sensitive equivalent” (the token). By tokenizing sensitive data, analysts can extract insights without the risk of exposing confidential information. Companies must strive to implement security and privacy protection that travels along with the data across a variety of hosting devices, models, and locations. 

Security should be prioritized by organizations since data contains sensitive information. They should do their best to safeguard their systems to prevent data breaches and cyberattacks. Technology did make everything more interconnected, but it also spawned new threats.