The Constrictor Knot of Big Data Security
With enormous amounts of data being collected, processed, and stored every day, big data has become the need of the hour for businesses across many industries. Big data is collected from various sources like mobile devices, e-mails, cloud applications, and servers. These big data infrastructures may hold personal information such as their age and others from their browsing history collected from customers to make their shopping experience personalized; or even health information retrieved from EMRs are stored. The smarter companies get in innovating their collection and analysis of big data, hackers are getting equally smarter in deploying attacks that put sensitive information at risk.
Encryption, centralized key management, user access control, intrusion detection and prevention, and physical security are some of the tools that are exclusively developed to manage the security of big data. With open source development communities and other proprietary software teams constantly wanting to stay ahead of the cybercrime perpetrators, the functionalities of these tools are enhanced with each passing day.
Meanwhile, these big data security tools suffer from several deficiencies which pave the way for loopholes in the system. These include newer technologies in active development such as advanced analytic tools and non-relational databases, which need advanced security processes that are complicated to implement and configure. While some of these tools may effectively protect the incoming data and storage, they may not have the desired impact on data output. Mining data without authorization can be done out of curiosity or for criminal profit; these suspicious activities should be monitored for better security. Cluster-based big data platforms introduce multiple vulnerabilities across various nodes and servers. Finally, if security patches are not updated and applied to the software periodically, big data owners have a huge risk of data loss and brand damage.
Additionally, focusing on application security rather than system security can also help secure big data and introducing real-time monitoring can enhance big data security. Well-monitored big data platforms, along with best practices, and employee awareness can serve every industry well for many years.
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