What is big data security? Challenges, solutions, and best practices

What is big data security?

Big data security is the practice of protecting data sets that are too large, complex, or variable to be handled by usual data processing methods. Modern organizations increasingly gather data from more sources and in more significant amounts. As such, big data sets have become critical assets and require specialized protection to address associated vulnerabilities.

The sheer scale of big data presents significant security issues. A large data set is always a prime target for cyberattackers and unauthorized parties seeking access. Since big data repositories often contain sensitive and high-value information, securing big data through data protection strategies has become a critical priority for IT systems worldwide.

Big data security solutions can function as standalone tools (like cloud security platforms and data encryption services) or be adopted as part of a larger data security strategy.

How does big data security work?

Big data security works primarily through configuring user access control. Access control solutions are frequently employed as a core component of big data security solutions. These solutions reduce the risks of data breaches and function as a comprehensive defense against a wide range of security threats.

Incoming data

Almost all big data sets are gathered in real time, so data is continuously transmitted from various data sources to data collection centers. Because these data sources often exist outside an organization's IT infrastructure, the incoming data is vulnerable to attacks during transit.

Big data security shields data in transit using approaches like intrusion detection systems (IDSs) and other similar methods of monitoring incoming network traffic. For highly sensitive data that requires controlled access, more aggressive or proactive security solutions (like IDS vs. IPS) can also be used to control the flow of information.

Data storage centers

The rise of big data analytics requires data storage systems like data lakes to hold big data sets as they're being collected and analyzed. These systems are also a prime target of cyberattackers because they often contain customer data and financial information.

Solutions for securing data storage systems primarily focus on user access control, though they also extend to the tools used to analyze the stored data. These strong security measures (usually cloud security) are used because data storage systems can be accessed at multiple points across a network, especially if numerous personnel or systems are involved in data analysis.

Analyzed output data

Security measures for big data also protect the processed and analyzed data output from big data systems. Data breaches often target output data since they contain the raw input data and additional insights about the data after processing it.

Big data security management largely relies on access control to limit risks. However, encryption, employee training, and automation are also crucial in securing analyzed big data sets. These measures ensure that data analysis delivers value to the organization without compromising privacy and security.

Big data security benefits

By securing big data, companies can safely use it in their day-to-day and long-term operations without fear of data breaches and other incidents. This process significantly improves operational efficiency and ensures that big data remains a reliable and integral resource for an organization.

Other benefits of big data security are that it:

• Reduces data access risks. A proactive approach to big data safeguards against internal and external threats, allowing an organization to leverage its big data sets.
• Improves customer confidence. Big data systems tend to store sensitive data, such as customer information. By securing big data, companies can strengthen customer trust in their ability to protect customer data.
• Ensures compliance with global data regulations. Many jurisdictions worldwide require companies to adhere to specific data protection laws, such as data confidentiality, to secure big data systems.
• Safeguards data integrity. Big data security systems ensure data integrity through audits, data loss prevention, and encryption — all business processes that ensure data accuracy.
• Enables proactive approach to security threats. A big data platform can deploy security measures, such as data privacy tools and monitoring systems, to monitor and respond to threats. 
• Optimizes operational costs. By deploying comprehensive big data security measures, organizations can centralize all their data security spending and reduce the risk of inconsistent protections for their big data systems.

Big data security challenges

Big data systems are rarely static. Between the different incoming data types, data analysis, and output data reports, the dynamic landscape of big data poses many challenges to data security. While access control and data governance are primary concerns that a big data security strategy aims to address, several other areas also present significant big data security issues.

• Insider threats. Anyone who has been given access to big data systems can potentially abuse their access privileges, requiring more monitoring and real-time response to prevent attacks like data exfiltration from insider threats.
• Security vulnerabilities. The sheer volume, complexity, and speed of big data can be effectively managed with proper configuration and use. However, these factors also increase security risks, especially with multiple points of failure across the system.
• Encryption challenges. Because of its size, encrypting big data can be tricky. Organizations must balance the need to protect sensitive data while ensuring that these measures don’t disrupt the functionality or performance of their big data systems.
• Manipulated input data. Attackers can attempt to compromise big data security by deliberately introducing false data into the system. Because of the added data volume, this can overwhelm security measures and potentially jeopardize the integrity of untampered data.
• Distributed protection. Big data is rarely accessed from a single entry point, with many companies using multiple locations to access it or implementing approaches like the Hadoop distributed file system. Big security solutions must account for all nodes in any distributed system, especially regarding access control.
• System adoption. Many companies switching to big data systems and security may need help implementing the advanced security measures that big data requires. This situation leaves them vulnerable to data breaches and other cyberattacks that exploit these gaps in security.
• Constant updates. Big data security requires regular updates to ensure real-time protection. An organization must adhere to a consistent update schedule to ensure that emerging threats are effectively mitigated and don’t bypass existing security measures.

Big data security best practices

Big data security isn't limited to the tools used for data security. It also refers to the approaches and other practices integral to effectively using these tools to protect sensitive data. Some of these practices include:

Access control

Big data security implementation must prioritize user access control. This practice ensures that only authorized personnel access big data systems and that administrators can assign and monitor these permissions over time.

Data encryption

Encryptions like the International Data Encryption Algorithm (IDEA) and the Advanced Encryption Standard (AES) can help protect data in storage or transit. These encryption techniques ensure that even if data was accessed, it would be useless to anyone who acquired it without the corresponding decryption key or protocol.

Network monitoring

Big data systems experience constant network traffic, and monitoring systems are essential to improve their security. By checking for unusual network behavior, administrators and security tools can immediately zero in on any potential threats and mitigate them before they access any data.

Data audits

Regular security audits and penetration testing can ensure that database management systems are strong enough to withstand potential attacks. These practices also ensure that an accurate and real-time record of data integrity is maintained, further improving data retention and accuracy.

Incident response and data recovery

Big data security practices must include an incident response plan to address successful attacks, regardless of their scale. Data recovery protocols are also essential, ensuring operations can continue while the attack is investigated or fixed.

Training and governance

Employees play a critical role in big data security. By training employees in best security practices, such as getting a VPN and installing malware blockers on their devices, a company can significantly reduce the risk of human error compromising its big data security efforts.

Finding a security provider

If a company lacks the time, resources, or training to create a big data security system, partnering with a big data security provider is also a good option. Companies may choose to use the services of a third-party platform or provider to access a broader range of security solutions.

How to implement big data security

Implementing prevention systems is only one part of big data security. A range of ongoing processes must be consistently maintained to ensure adequate protection. While specific approaches may differ between companies, the following practices provide a strong framework for big data security implementation:

• Centralization. Centralizing security measures can enhance the efficiency and security of big data systems. By managing encryption keys from a single location or having a core committee for assigning data governance and user access control, organizations can better implement the best practices for big data protection.
• Employee training and upskilling. It's important to ensure that trained and certified employees manage the centralization of big data security solutions. Organizations must equip employees with the technical skills to operate big data security solutions and train them to convey the importance of these tools and practices to the rest of the organization.
• Regular audits and security monitoring. Regular network security assessments can bolster an organization's big data security in real time, especially for data management systems. Keeping these records ensures proper data governance and supports compliance with regulatory standards for the responsible handling of big data.
• Streamlined updates to security tools. As attacks on big data grow more frequent and sophisticated, an organization needs to ensure its protections are up to date. While automating this process provides the most reliable protection, regular and manual reviews can also enhance security by identifying vulnerabilities that may have been overlooked.
• Integrating big data security into business processes. Business goals and needs should never take precedence over the security of big data systems. Integrating big data security measures into business processes reduces the likelihood of security protocols clashing with the functionality of big data systems.
• Adoption of new systems and technologies. Organizations must always stay updated with the latest security trends in protecting big data systems. Approaches like cloud security monitoring or accounting for user-generated data are crucial for ensuring long-term protection.

Who’s responsible for ensuring big data security?

Big data security is primarily the responsibility of a company's IT team. Ideally, this team should have specialists proficient in the company's big data security tools, effective communicators capable of engaging with other departments on big data security best practices, and a support staff that can handle inquiries both from inside and outside the organization.

Additionally, third parties, such as big data security providers, can support big data security strategies or function as the sole caretakers of big data systems. This approach has specific considerations but can also offer key benefits like network traffic analysis or advanced intrusion detection systems, which most companies may not have the resources to implement in-house.