Big Data Security Tools for Safeguarding Big Data Driven Enterprises

By Ashutosh Sable
6 Min Read

Big data security is a process that includes all the measures and tools for protecting data and analytics processes, both on cloud and on premise servers, from attacks, theft, or any other activity that could harm an the organization’s confidentiality. The global enterprises are tremendously adopting big data security and using powerful analytics to enhance their decision making, identify lucrative opportunities, and improve performance.


There are three major big data security practices that define an enterprise’s BI security system and play an important role in creating a flexible end-to-end big data security system for an organization. BI security platform consists of almost all important information about the business, thereby is very important to safeguard it.  The first security practice is for the incoming data, which can be corrupted in transit. The next one is for the stored data, which can be stolen from on cloud or on premise servers and the last practice is for the output data.

Big data security technologies:-

  • Encryption- Encryption tools are used to format or code a massive volume of data. The code can either be user generated or device bred.
  • User Access Control- It is a basic network security tool but involves high management overhead, and thus is less adopted by global companies.
  • Physical Security- It is implemented when the user deploys the big data platform in his personal center. Video surveillance and security logs are used in this technology.
  • Centralized Key Management-It is the best security practice among all. It is mostly beneficial for the enterprises who have wide geographical distribution. Policy driven automation, on demand key delivery, logging, and abstracting key management are used in this technology.

Big data security representatives:-

  • CDAP- Cloudwick Data Analytics Platform consists of varieties of security and safety features from numerous analytics toolsets like Intrusion Detection System (IDS) & Intrusion Prevention System (IPS), and device learning projects.
  • IBM- IBM Guardium Data Protection for big data includes discovery and categorization of data, vulnerability assessment, data activity monitoring, and others.
  • Logtrust- It has a unique authentication between collectors and processors through which it is able to ensure end-to-end protection. It is on partnership with Panda security to provide Advanced Reporting Tool (ART) and Panda Adaptive Defense (PAD).
  • Gemalto- Gemalto Safe Net protects big data platforms in cloud, data center, and online environments. It includes digital signing solutions, strong authentication, data encryption, and others.

Big data security use cases:-

  • Cloud security Monitoring- This is a practice of continuous supervision of both online and offline servers in order to offer secured communication and profitable business.
  • Threat Hunting- Manual processes of threat hunting are time consuming. So to speed it up, organizations use security analytics which acts as an extra set of eyes to automate threat of hunting.
  • Network Traffic Analysis- Exchanging large volume of data over the network prevents transactional visibility. NTA is a practice of monitoring network accessibility to determine operational issues. It helps enterprises watch over the network traffic, check real time, and track historical network records. It also helps clarify the hidden dark spaces in cloud infrastructure and ensures proper working of channels.
  • Insider Threat Decision- Threats to the system from insider are no less dangerous than external threats. Security analytics can easily determine insider threats through behaviors like unusual email usage, unauthorized database access, uncertain login times, and others.
  • Data Exfiltration Detection- It is usually used to determine any leakage of data in communication channel.
  • User Behavior Analysis- It is important to track user’s behavior in order to decide the success or failure of cybersecurity.  So, security analytics keeps an eye on unusual behavior of employees.

Current market scenario of global big data security market:-

Undeniably, in the upcoming years, the world will be digitally transformed and big data will be the core hub of all kinds of businesses. According to a report published by Allied Market Research, the global big data security market size is projected to reach $54,237 million with a considerable CAGR from 2020 to 2027.

Now-a-days, most of the global industries are indulged in collecting, creating, storing, and controlling large volume of data. With the rise in number of cyber-attacks, big data security has gained a lot of importance among such large companies, which in turn, is tremendously boosting the global market of big data security.

Furthermore, with the advancements in computing technologies, the demand for multiple edge devices like IoT sensors, robots, remote servers, and big data security are increasing across the world. With this drift on board, the key market players are trying to bring certain advancements within the big data techniques, which sequentially, is anticipated to offer lucrative opportunities for the global market growth in the near future.

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