Big Data Security: Adequate Measures to Prevent Real-time Cyber Threats
Big data has exploded due to the confluence of internet connectivity, wireless devices, large storage options, and even a bigger number of users. It has found a wealth of applications in industries ranging from farming to oil & gas. But the accumulation of data for research and analysis purposes has led it to be vulnerable to breaches and constant attacks. Companies are either unwilling to invest in its security or ready to dispense untold sums in terms of ransom. Big data security Market has now gained prominent attention due to stringent guidelines and laws of various nations to safeguard their citizen’s data and privacy and forecast periods 2020 t0 2027.

Concerns of Big Data
The volume of data generated in data lakes by devices and machines has led to gross negligence by companies over its security. This has led to breaches and cyber attacks on these servers hosting proprietary data.
• Fake Data
Fake data has been generated and deposited with real data with processors unable to distinguish between genuine and dishonest data. This has led to confusion and created problems with identity.
• Cloud Computing
Companies have started hosting data on the cloud to save overhead costs on physical servers. But this led to many endpoints open for attack. The risk is bound to increase with a majority of clients looking to the cloud to solve their data storage and processing problems.
• Employee Negligence
Employee negligence owing to logging in to malicious sites and use of external drives for transfer and storage of data can create room for hackers.
• Data Mining
Data mining is a cause of concern as the contents can be used in doxing employees or employers. Presence of data reversing tools for unmasking confidential data can harm companies in the long run and lower trust among clients and customers.
Big data security aims to implement stringent workplace policies and strengthen network security to lower chances of attacks. But analytical models noted for monitoring our data and providing observations for strengthening the system are being adapted by organizations.
Analytical Models
In a bid to improve the defects in security systems put in place by organizations, big security data analytic models are being employed to simulate tests to improve its defenses. Rise in cyber threats and large number of cyber breaches have led to organizations establishing models fueled with artificial intelligence and deep learning to accelerate the learnings.
Intrusion prevention systems can facilitate teams in monitoring network traffic and prevent the entry of malicious actors. It can be used in detecting patterns, which are then used in running correlations from a variety of data sources to pinpoint threats. The tools in the system operate in real-time and rank threats according to their severity level. Those alerts are used to isolate and alleviate threats by security teams.

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Compliance with Data Privacy Laws
Organizations have to adhere to changes in data privacy laws owing to security concerns by governments on misuse of data. This has led to a showdown between large software corporations and governments with arguments ending in a stalemate. The calls for stronger data privacy and protection laws have led to the European Union, the United States, and China rolling out laws supporting the same.
The General Data Protection Regulation (GDPR) by the EU has dictated companies to outright place individual rights over company objectives. This has led to transparency among citizens and minimal collection of data. Similarly, the China’s National People’s Congress (NPC) rolled out a data protection law to protect the data of citizens and permits its use to accelerate digitalization of the domestic economy.
Enterprises need to implement big data security solutions to protect their data and prevent the formation of any vulnerable endpoints.

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