Big data's security is a worrying concern for many companies because a single dangerous attack might leave your organisation vulnerable.
Big data exhibits the 4 V's (volume, variety, velocity, veracity). Big data applications are innumerable, ranging from banking, healthcare, insurance, pension, and governmental proceedings, due to its numerous advantages. Every chunk of data in an organisation is crucial, and it is important to manage it securely. Big data security is one of the major concerns faced by any sector today, leading organisations into using more scalable tools to meet the security constraints.
Big data technology ingests large chunks of data, which lead involves significant risk to database security as containing such large volumes of critical data can result in a data breach. These data breaches may involve useful information such as credit card details, bank details, and various other personal information, a theft of which may cause devastating consequences. These data breaches may lead end users into distrusting their organisations. This highlights the need for more scalable big data tools, which will reduce these data thefts. The purpose of big data security is to build a firewall for unauthorised users, keep strong user-authentication, and ensure end-user training. Additionally, it aims at providing intrusion protection systems and intrusion detection systems, which operate at all data stages. The list below highlights ways that organisations can use with big data to resolve issues regarding security:
Distributed computing frameworks such as Spark, Hadoop, MPI have a considerable risk of data leakage. Additionally, they have a chance of being associated with untrusted mappers. Cloud Security Alliance (CSA) recommends companies to use authentication methods and establish trust. Furthermore, de-identification must be inculcated to ensure privacy constraints are met. Then, organisations must validate access to files and ensure that sensitive data is not leaked by any means.
Data must be stored in a secure way to enhance big data security. To secure data storage, a technique called secure untrusted data repository (SUNDR) must be employed to monitor unauthorised alterations from third party agents.
While organisations assimilate large chunks of data to improve their services, collecting data is a difficult and expensive task. To secure your data, organisations must use firewall security, intrusion detection and prevention tools, scanning tools, and demand validation for all access to data.
Auditing is a must with big data security, and it is vital to maintain the audit data separately for future reference. Post any attack, organisations must conduct a complete audit to check whether operations are working fine. Technologies like Apache Oozie can help to understand big data clusters better.
Hardware or software malfunction is one of the important causes of data loss in any organisation. Hence, it is essential that organisations manage hardware and software configurations by ensuring it is updated regularly. Preventing data breaches is one of the processes that organisations are inculcating in their culture with scalable big data analytical tools. Organisations must secure their big data platforms from threats to serve their business without interruption for years.
Naveen is the Founder and CEO of Allerin, a software solutions provider that delivers innovative and agile solutions that enable to automate, inspire and impress. He is a seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. He is currently working on Internet of Things solutions with Big Data Analytics. Naveen completed his programming qualifications in various Indian institutes.