Big Data Privacy And Security Challenges Post COVID

The IDC (International Data Corporation) has declared that a rise in global IT expenditure may go down by 3 to 4% in 2020. The horrible scenario created after the outbreak of COVID19 is the primary cause behind this minor drop. 

The pandemic may end soon but will have its disastrous effect left for long in the big data and cloud computing world post COVID. This fact was exclusively confirmed in IDC Worldwide Black Book Live Edition in February 2020. The effect might also become visible as unexpected challenges in data privacy and security zones when many countries are busy recovering from COVID-19.

While COVID is likely to affect Software, Hardware, and Services businesses equally, they are all expected to slip into a retarded phase as the fight against Corona virus continues across international countries. However, the implementation of security solutions, collaborative applications, Big Data, and Artificial Intelligence is about to experience a rise in the post-COVID phase. 

The pandemic brings a list of precautionary measures to IT vendors so that they can stay away from COVID induced data privacy and security challenges, maintaining client confidentiality.

What is Big Data & Where is It Used?

Big Data doesn’t just indicate the data size. It is a term used to refer to a massive volume of data (both unstructured and structured datasets) that is so big that it gets difficult to process them with traditional software and database methods. In many situations, the volume of such data is too large, or it travels so fast, or it goes over present processing capability.

Big data addition and usage helps to target and re-target the relevant products, services, customers, customize their experience, sort out their pain points, or make products matching their needs. Amazon, Netflix, Coca-Cola, and many more big brands are leveraging big data computing to enhance customer care service in different ways. 

However, the effect of COVID on this significant volume of data can give rise to data privacy problems, alarming organizations more about Big Data Security. Expecting a recovery from COVID by 2020, many IT vendors are acknowledging the worst effects in the form of data privacy and security threats. Most of them are keeping ready with precautionary measures to avoid them in the first instance.

Big Data Security Concerns – Why Do They Arise?

Big data is common to large corporations; however, its popularity and usage are not less either in small and medium-sized organizations due to low cost and ease of managing data.

Cloud storage has made data mining and accumulation easier. However, real-time integration of cloud storage and big data has given rise to data privacy and security challenges. The cause of such threats maybe just because the security apps are designed to store limited amounts of data and can’t process the datasets exceeding its data limit. 

Again, such security software can handle static data but is useless when it comes to managing dynamic data. Hence it is hard to find security breaches for continuously streaming data with any regular security checks. That’s why; round the clock data privacy is needed while using big data analytics, and data are streaming continuously.

Potential big data privacy and security challenges after COVID

Traditional security systems are tailored to protect small-size static but are not enough for extensive size streaming data. For instance, the analytics used for abnormality detection would produce several outliers. Likewise, the process of retrofitting data provenance in present cloud infrastructures is not very clear. Streaming of data requires super-fast response times from big data security and privacy solutions. Here, we discuss the potential big data security and privacy challenges post-COVID.

1. Protect data and transaction logs storage

Multi-tiered storage mediums are used for storing transaction logs and data. The manual transfer of data between different tiers is easy to track by the IT manager. But the continuous transmission of data sets adds to its size, makes it grow big, and needs auto-tiering solutions to manage the big data storage successfully. However, protecting the big data storage with auto-tiering becomes challenging due to difficulty in tracking the location of storage.

2. Immediate security/compliance checking

These produce a high volume of information; it’s better to find out a way to overlook the fake positives, so IT talent can be used on identifying the right security breaches.

3. Secure distributed framework processing

Distributed big data processing across multiple frameworks may lead to fewer data processing by a single system, but giving rise to lots of security concerns at the same time.

4. Best practices for security of non-relational data stores

Think of non-relational NoSQL databases, where security lacks, and data privacy risks are an all-time high.

5. Validation and filtering of Endpoint input 

All the necessary activities, including storage and processing, are done with data inputs drawn from endpoints. So, it is required to validate the dependability of those endpoints.

6. Preserve data mining – scalable and compassable privacy

Big data can be a disturbing sign of potential attacks on privacy, un-preserved marketing, less social freedoms, and an increase in corporate and state control. So, there is always the risk of a privacy leak.

7. Cryptographically secure communication and access control

To make sure only authenticated entities have access to sensitive data, a cryptographically secure (Attribute Base Encryption) access control policy, and communication has to be applied.

8. Granular access control for data secrecy

Granular access control mechanisms should be adapted more to help data managers share as much data as possible without breaking secrecy.

9. Granular audits

Auditing different data objects help to learn the scope of cyber attacks as well as compliance and regulatory issues. Therefore, granular auditing can be helpful.  

10. Data provenance

It is always computationally exhaustive to analyze large data provenance graphs to identify metadata dependence for confidentiality/security applications.

Conclusion:

Organizations need to make sure that all big data environments are protected against security breaches and vulnerabilities post COVID. From data collection and transfer to processing, all precautionary measures, including real-time data management, should be followed. Organizations should not forget that managing a massive volume of data could be challenging post-COVID and needs exceptional efforts. However, considering all the above challenges and taking preventive measures would keep customer privacy intact.