Top 7 Big Data Security Issues and Their Solutions

Today, Big Data is crucial for any business to succeed in the data-driven world. With several advancDeploying highly secured databases is vital to ensure data security at all levels.ed infrastructures, organizations have streamlined the flow of data for real-time insights delivery and better decision-making. However, Big Data brings several security risks that could negatively impact organizations. Failing to incorporate security measures while storing and processing Big Data can lead to data breaches. While simplifying the accessibility of data is essential for companies, having control over Big Data is equally crucial for ensuring trust among its customers.

The article begins with a brief introduction to Big Data and its benefits before it dives into the 7 critical challenges faced by Big Data Security. It also offers simple solutions to deal with these challenges.

Introduction to Big Data

Big Data is large, diversified sets of data sourcing out of multiple channels: social media platforms, websites, electronic check-ins, sensors, product purchase, call logs, the choices are limitless. Big Data has three unique characteristics: volume, velocity, and variety.

• Volume: Big Data contains an undecided and unfiltered volume of information. The data collected is different for different businesses. Therefore, the efforts paid are unique. Nonetheless, filtering valuable data from the voluminous pile is essential. Companies need to process this high-volume information to address their business challenges. 
• Velocity: It is the speed at which data is created and collected. Mobile, SaaS solutions, e-commerce transactions, and IoT devices are a few of the primary sources of acquiring real-time data. The velocity at which data is generated at scale requires real-time handling and processing for augmenting Data Analytics.
• Variety: Conventional data types consist of structured data that fit well with relational databases. However, with Semi-structured and Unstructured data in the landscape, the information received requires additional preprocessing to convert it into digestible formats. While Structured data can be quickly dealt with, Semi-structured and Unstructured data need to be converted into predetermined models or formats before turning them into actionable information.

Processing Big Data has become the go-to technique to collect information that can further be used to enhance business operations. However, the process is not straightforward. Considering its diversified nature and content, traditional relational databases are incapable of capturing, managing, or processing Big Data into digestible formats.

Understanding the Benefits of Big Data

Data Analysts harness different data types primarily to make better and improved business decisions by understanding customer behavior and their purchasing patterns. Data Mining, Machine Learning, and Predictive Analytics are a few of the newly-evolved techniques used to achieve new insights into untapped data source areas for optimizing business processes. Let’s discuss the main benefits that businesses can reap from Big Data:

• Big Data allows companies to improve their products and create tailored marketing by gaining a 360-degree view of their customers’ behavior and motivations.
• It enables businesses or service providers to monitor fraudulent activities in real-time by identifying unusual patterns and behavior with the help of Predictive Analytics.
• It drives supply chain efficiencies by collecting and analyzing data to determine if products are reaching their destination in the desired conditions to attract customers’ interest.
• Predictive analysis allows businesses to scan and analyze social media feeds to understand the sentiment among customers.
• Companies that collect a large amount of data have a better chance to explore the untapped area alongside conducting a more profound and richer analysis to benefit all stakeholders.
• The faster and better a business understands its customer, the greater benefits it reaps. Big Data is used to train Machine Learning models to identify patterns and make informed decisions with minimal or no human intervention.

Understanding the Challenges of Big Data Security

In the rapidly evolving world of big data, maintaining security is critical. As organizations deal with varying data sources and structures, they must also manage potential incidents swiftly. The core functions of an incident response team become essential in designing effective strategies to manage these threats. Understanding roles like Security Analyst and Incident Response Manager can empower organizations to enhance their data protection measures and reinforce their understanding of potential vulnerabilities. Here are some of the Big Data Security challenges that companies should mitigate:

• Data Storage
• Fake Data
• Data Privacy
• Data Management
• Data Access Control
• Data Poisoning
• Employee Theft

1. Data Storage

Businesses are adopting Cloud Data Storage to move their data easily to expedite business operations. However, the risks involved are exponential with security issues. Even the slightest mistake in controlling the access of data can allow anyone to get a host of sensitive data. As a result, big tech companies embrace both on-premise and Cloud Data Storage to obtain security as well as flexibility.

While mission-critical information can be stored in on-premise databases, less sensitive data is kept in the cloud for ease of use. However, to implement security policies in on-premise databases, companies require cybersecurity experts. Although it increases the cost of managing data in on-premise databases, companies must not take security risks for granted by storing every data in the cloud.

2. Fake Data

Fake Data generation poses a severe threat to businesses as it consumes time that otherwise could be spent to identify or solve other pressing issues. There is more scope for leveraging inaccurate information on a very large scale, as assessing individual data points can be a daunting task for companies.

False flags for fake Data can also drive unnecessary actions that can potentially lower production or other critical processes required for running businesses. One way to avoid this is to ensure that companies should be critical of the data they are working on for enhancing business processes. An ideal approach is to validate the data sources by periodic assessments and evaluate Machine Learning models with diverse test datasets to find anomalies.

3. Data Privacy

Data Privacy is a big challenge in this digital world. It aims to safeguard personal or sensitive information from cyberattacks, breaches, and intentional or unintentional data loss. Businesses must follow stricter Data Privacy principles with the help of access management services in the cloud, including very rigid privacy compliance, to strengthen Data Protection. It is best to follow a few rules alongside implementing one or more Data Security technologies. The general rules are knowing your data, having more grip over your data stores and backup, safeguarding your network against unauthorized access, conducting regular risk assessments, and training the users regularly about Data Privacy and Data Security.

4. Data Management

A security breach can have crushing consequences on businesses, including the vulnerability of critical business information to a completely compromised database. Deploying highly secured databases, complemented by Data Security Posture Management (DSPM) practices, is vital to ensure data security at all levels by continuously monitoring and addressing potential vulnerabilities. A superior Database Management System comes with various access controls. While it is advisable to follow rigid and rigorous physical security practices, it is even more essential to follow extensive software-based security measures to safeguard data storage. A few methods to effectively achieve this goal are—practicing data encryption, data segmenting and partitioning, securing on-the-move, and implementing a trusted server. Besides, a few security tools can integrate with databases to automatically monitor data sharing and notify businesses when data has been compromised.

5. Data Access Control

Controlling which data users can view or edit enables companies to ensure not only data integrity but also preserves its privacy. But managing access control is not straightforward, especially in larger companies that have thousands of employees. However, a shift from on-premise solutions to cloud-based services has simplified the process of working with Identity Access Management (IAM). IAM does the job of controlling data flow via identification, authentication, and authorization. Following relevant ISO standards is a good starting place to ensure organizations meet the best IAM practices. 

6. Data Poisoning

Today, there are several Machine Learning solutions like chatbots that are trained on a colossal amount of data. The advantages of such solutions are that they keep on improving as users interact. However, this leads to Data Poisoning, a technique to attack Machine Learning models’ training data. It can be considered as an integrity attack as the tampered training data can affect the model’s ability to provide correct predictions. The results can be catastrophic, ranging from logic corruption to Data Manipulation and Data Injection. The best way to beat the evasion is through outlier detection, wherein the injected elements in the training pool can get separated from the existing data distribution.

7. Employee Theft

Advance data culture has allowed every employee to hold a certain level of critical business information. While it boosts data democratization, the risk of an employee leaking sensitive information, intentionally or unintentionally, is high. Employee Theft is prevalent not only in big tech companies but also in startups. To avoid Employee Theft, companies have to implement legal policies along with securing the network with a virtual private network. In addition, companies can use a Desktop as a Service (DaaS) to eliminate the functionalities of data stored in local drives.

Conclusion

Based on the enlisted concerns, it is apparent as to why enterprises are seeing Big Data Security as a major concern. However, the good news is that with the right information, resources, skilled manpower, detailed coping strategy, and commitment towards data integrity and privacy, many of such challenges can be easily addressed. The absence of threats to Big Data will lead businesses to achieve their ultimate goal of harnessing data for better customer experience and enhanced customer retention.

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