Big Data Security Management: Best Practices for Threat Detection and Mitigation

Big data evolved together with online capabilities, offering organizations next-gen analytical, promotional and adaptive capabilities. At the same time, these advancements created new data protection challenges. Given the volume, variety and sensitivity of collected records, big data security is nowadays a crucial concern for those who either own or operate data assets.

The complexity of such assets makes building an effective protection system impossible without proper big data security management. The approaches must be implemented correctly to adjust and control solutions and practices according to the specifics of a particular database. In this post, we discover what big data is, what the main big data security challenges are, and which security practices IT industry experts most frequently recommend.

What is Big Data?

Big data means big and diverse datasets collected from various sources available online. Social media, websites, sensors, online purchase tracks, GPS location, emails, phone numbers, route patterns, customer feedback, among other data are all used to create and then analyze big data. 

Generally, the IT industry recognizes three main features characterizing big data. Those are the so-called “Three Vs”: 

• Volume
• Variety
• Velocity

Volume

Big data consists of a large number of records that are unfiltered and undefined. Every organization collects data according to their specific needs, thus the efforts and sources are unique for every business. Still, being either structured or unstructured, big data requires further processing, making appropriate protection and management more challenging with every new data type, amount and source added.

Velocity

Velocity characterizes the speed of data creation and accumulation. Modern technologies enable adding new records to big data assets in real time via mobile devices, cloud and SaaS solutions, IoT gadgets, and other nodes. Consequently, the current velocity of database refreshing rates requires security management to keep up the pace of changes and react to threats accordingly. 

Variety

Regular data assets include structured records that can be swiftly used within a relational database. Big data, in turn, contains semi-structured and unstructured records that require processing like sorting, formatting and selection before purposeful use.

Big Data Security Issues: Main Challenges to Consider 

Big data processing and management is complicated. Still, big data security challenges include other weaknesses and pain points that specialists should address when building and operating big data security management systems. 

Storage

To increase operational efficiency, organizations tend to use cloud storage for better data exchange speed and convenience. As cloud storage is always online, security risks are growing exponentially for every record moved and residing in such storage. Consequently, on-premise storage is still in the game, as the more reliable and secure alternative to the cloud. 

The specifics of different storage types make big data security management a separate discipline in data protection, requiring cybersecurity expertise and well-thought workflows. Security measures working for cloud storage may not be equally effective for on-premise repositories and vice versa. Given the need to use both storage types, organizations must have an in-depth understanding of their infrastructures and risks.

If you’re looking to build a career in Cybersecurity, check out our latest blog: 5 Tips for Starting Your Cyber Security Analyst Career.

Credibility

The issue of fake data poses a threat to the overall quality of databases and is directly connected to big data analytics security. Analytical activities based on fake data lead to false outcomes and, consequently, bad decisions on projects or evolution of an organization. This can result in reduced production workflows or critical processes failures and other unwanted consequences. 

Fake data can originate from both reputable and untrustworthy sources, getting mixed with real data and turning the entire big data array into an unusable yet costly asset. Thus, distinguishing the credibility of records is another big data and security challenge. 

Privacy

The overwhelming digitalization of the world has made privacy a critical issue regardless of data types. Data privacy is called to keep sensitive or personal records safe from theft, improper use, accidental and intentional loss. Organizations must keep up with data privacy and protection regulations not only to avoid serious fines and legal penalties but also to prevent reputational damage. 

Big data security is also about keeping those data volumes private. In the case of big data, privacy can be crucial not only for an organization’s clients and partners. Data assets can include intellectual property and data that is the ground for decision-makers to develop future development plans. Ensuring big data privacy is also difficult due to the complexity of storage requirements and usage workflows that security specialists have to deal with. 

Management

Big data security management comes along with database management in general because they should work well with each other. Protection systems must provide the required security without disrupting regular data management and maintenance workflows. On the other hand, to prevent exposing or creating big data security vulnerabilities, data management must be organized accordingly.

Access Control

Security management is impossible without access controls. This means that controlling which data a user can access and edit ensures data privacy and integrity. When dealing with big data security issues, access control and the principle of least privilege are effective approaches to keep data assets safe from third-party access. Again, the volume and complexity of databases pose a challenge for security specialists to allow employees to operate data efficiently without sacrificing protection. 

Data Theft

Data-dependent organizations (which means nearly all SMBs and enterprises existing today) provide every employee with access to some sensitive data to fulfill their duties. Although one can’t deny the increase in overall production efficiency due to that, data security risks connected to employees misusing those sensitive records are always a possibility. An employee that has access to, for instance, big data containing client information can be the source of a security breach and data leakage in favor of competitors. Big data security challenges include preventing potential fraudulent employees from causing significant damage to an organization’s confidentiality, revenue and reputation.

Big Data Security Solutions and Best Practices

After we have defined what big data is and have specified big data security issues that organizations need to solve, we can proceed with some practical recommendations and solutions. The practices suggested below are versatile. Consider implementing them in the security systems of your organization to enhance data protection and maintain control of valuable digital assets.  

Non-Relational Data Security

NoSQL and other non-relational databases are commonly used but vulnerable to NoSQL injection cyber attacks. To improve non-relational data protection, consider password hashing or encrypting. Also, using algorithms such as advanced encryption standards (AES), RSA, or Safe Hash Algorithm 2 can help you maintain end-to-end data encryption to prevent third-party interception.

Data Storage and Transaction Logs Monitoring

The reliability of storage critically depends on storage control systems. Consider implementing a data storage control solution that can enable your IT security team to track data creation, transaction and alteration in real time. Thus, your awareness of data operations increases, allowing you to know typical patterns and notice when something goes wrong. With that knowledge, your reaction time is enhanced in case of potential emergencies, and you can start a response workflow to counter or at least mitigate the consequences of threats like ransomware attacks. 

Validation and Filtering of Endpoint Devices

An organization can have hundreds or even thousands of different devices connected to big data storage for production purposes. Those can be corporate or private smartphones of employees, their desktop and laptop computers, and tablets, among other gadgets. With device management solutions, you can enhance access security with trusted credentials, verify resources and provide internal network access only to trusted nodes. 

Real-Time Monitoring for Compliance and Security Systems

With networks and environments being complex and dynamically changing, constant monitoring of the devices’ trustworthiness is critical for big data security. Consider using Kerberos or other authentication protocols to verify the identities of connected nodes and monitor their activities inside the organization’s environment. Pay careful attention to log monitoring and security mechanisms at application, network and cloud levels. 

Data Privacy and Employee Education

Concentrating on employee education and maintaining the required level of knowledge on data privacy helps to reduce human errors. Employees who know about both typical and new hacking techniques, privacy regulations and possible consequences, are less likely to create or expose security vulnerabilities leading to breaches, data theft and loss.

Big Data Encryption

Unencrypted data can lead to leakage or unwanted access. Modern solutions enable continuous data encryption, preventing third-party access during transmission and storage periods. A solution providing AES or SSE encryption is a must-have for any big data security system. 

Thoroughly Organized Access Control

Access control solutions enable the implementation of the principle of least privilege (PoLP), thus minimizing the rights of employees to read, operate and change the records. Limiting user rights to those required to effectively perform their job responsibilities means that a hacker can’t cause critical harm via a single compromised account.  

Data Backup

Regardless of the security solutions you apply to prevent or counter cyberattacks, there is always a risk of protection failure. When big data arrays are lost, the only measure that can save an organization from devastating consequences is a timely created and updated backup. 

Backing up big data manually or with legacy solutions is a task that would take too much time and effort. Implementing a comprehensive backup solution like NAKIVO Backup & Replication is critical for organizations to enable an effective backup and recovery process. With such a solution, you can automate backup, backup copy, recovery and replication workflows to significantly improve your big data security management capabilities.

Conclusion

Big data is a large, rapidly changing and diversified data array that is constantly threatened by cyberattacks or failures causing data leakage, loss or theft. The very nature of enormous datasets, the complexity of IT environments and the variety of threats make big data security management a challenge for qualified specialists. To enhance the protection of an organization’s valuable assets and maintain the efficiency of data security management, consider implementing the following measures:

Non-relational data security;

Real-time monitoring for storage, logs and activities;

Validation and filtering of endpoint devices;

Employee education and data compliance training;

Data encryption during transmission and retention;

Strict access controls and principle of least privilege;

Reliable and regularly-updated data backups.