Big Data Security is the process of monitoring and securing an organization’s vital data keeping in mind the compliance standards and regulations to ensure the safe usage of data for ongoing operations.

Organizations deploying Big data security are a direct target of malicious attackers. A single ransom attack can lead to a huge data and reputational loss for the organization. When an unauthorized user gains access to the company’s big data, valuable information is at the risk of falling into the wrong hands leading to identity theft and many more cybercrimes. Big data security platforms are a mixture of traditional security tools, newly developed toolsets, and intelligent processes for monitoring security throughout the life of the platform.

Technologies in Big Data Security

The forms of technologies that are used in big data security have been in use for quite some time, but the ability to secure multiple types of data at different stages has evolved.

1. Encryption
2. Centralized Key Management
3. User Access Control
4. Intrusion Detection and Prevention
5. Physical Security

Encryption:

The purpose of the encryption tool is to secure data in transit and at rest. This also has to be carried out across massive data volumes. It is necessary to be able to encrypt a wide range of different types of data, both those generated by individuals and machines. The encryption tool must also work with various analytics tools and their output data, as well as with common large data storage formats, including relational database management systems (RDBMS), non-relational databases like NoSQL, and specialized file systems like Hadoop Distributed File System (HDFS).

Centralized Key Management:

Security best practices recommend centralized key management. Big data environments, especially those with wide geographic distributions, are no exception. A policy-driven automation approach, logging, and key delivery on-demand are all best practices. Centralized key management helps ensure that keys are secure and that access is granted only to those with the appropriate authorization. It also reduces complexity and the need for manual processes and reduces the risk of unauthorized access.

User Access Control:

User access control is one of the simplest security tools, but many companies use it minimally due to the management overhead. At the network level, this is dangerous and can have devastating effects on the big data platform. An effective user access control approach must be based on policies that automate access based on the user’s and role’s settings. The big data platform is protected against inside attacks using policy-driven automation, which manages complex user control levels.

Intrusion Detection and Prevention:

An intrusion detection and prevention system is an essential piece of security infrastructure. The importance of big data platforms is not diminished as a result. Big data’s value and distributed architecture make it an attractive target for intrusion attempts. Security admins use IPS to protect the big data platform from intrusions, and IDS quarantines intrusions that succeed before they have a significant impact.

Physical Security:

Whether you deploy your big data platform yourself or carefully investigate the security of the cloud provider’s data centers, you must include it in the design of your platform. A physical security system can prevent strangers and unathorised employees from entering data centers. Security logs and video surveillance will have the same effect.

How does Big Data Security work?

Big data security’s mission is to keep out unauthorized users with the assistance of firewalls, strong user authentication, end-user training, and intrusion protection systems (IPS) and intrusion detection systems (IDS). If a malicious attacker does gain access, encrypt your data in transit and at rest.

This sounds like any network security strategy. However, big data environments add another level of security because security tools must operate during three data stages that are not all present in the network. These are data ingress, (which is what’s coming in), stored data, and data output (going out to applications and reports). Stage 1: Data Sources.

Big data comes from a myriad sources and different data types. The data generated by users can include CRM data, ERM data, transactional data, and even unstructured data from email or social media. Besides this, machine-generated data including logs and sensors is also available. This data needs to be protected in transit, from the source to the platform.

Stage 2: Stored Data.

To protect stored data, you need mature security tools like encryption at rest, strong authentication, and intrusion protection. It is necessary for a company to run its security tools over a distributed cluster platform that spans many servers and nodes. In addition, its analytics tools and log files must be protected by security tools.

Stage 3: Output Data.

A big data platform’s complexity and expense are entirely due to its ability to analyse massive data volumes and different types of data. The results of these analytics are output to applications, reports, and dashboards. A rich source of intelligence like this presents an extremely valuable target for intruders, so it is critical to encrypt both outputs and ingresses. It is also important at this stage to ensure that results going to the end user do not contain regulated information.

Benefits of Big Data Security

Companies have a lot to benefit from a big data security system with its advantages of customer retention, risk identification, business innovation, and cost-efficiency.

Customer Retention: When a company uses big data security, it can observe many data patterns, which allows it to better match its products and services with its clients’ requirements. Risk Identification: Using big data tools to identify risks in an infrastructure is made possible by big data security, which will assist companies in creating risk management strategies. Business Innovation: Big data security can help companies update their tools and transfer their products to new secure systems. By implementing this innovation, businesses can improve their marketing techniques, customer service, and productivity. Optimization of costs: The efficient storage, processing, and analysis of large volumes of data can lower customer costs. Using big data security tools, companies can also determine how the product will benefit their infrastructure and therefore choose a company accordingly.

Challenges Involved in Big Data Security

There are several challenges to securing big data that can compromise its security. It is important to keep in mind that these challenges are not limited to on-premise big data platforms. They also apply to the cloud. Establish a strong security service level agreement with the provider to overcome these challenges.

Some of the key challenges are:

New technologies can be vulnerable: Big data technologies in active development include analytical tools for unstructured big data and non-relational databases (NoSQL). In some cases, new toolsets can be difficult to protect by security software and processes. Variable impact: A mature security tool protects data from unauthorized access and storage. However, multiple analytics tools may not have the same impact on data output from multiple locations. Access without permission: Administrators of big data may mine data without permission or notification. In order to prevent suspicious access, regardless of the motivation, you will need to set up an alerting system in your security tool to prevent cyber threats and data breaches. Beyond routine audits: It is impossible to perform routine security audits on terabytes and petabytes of big data installations due to their size. It is also important to note that most big data platforms are cluster-based, which means multiple vulnerabilities can occur across many servers and nodes. Requires constant updates: Data loss and exposure are risks faced by big data owners who don’t regularly update security measures.

Conclusion 

Big data security has become a critical component in the ever-evolving security landscape. Implementing these measures allows organizations to detect potential threats quickly and accurately. Additionally, it provides them with the capability to analyse large volumes of data to identify patterns, trends, and correlations. This helps organizations stay ahead of the game and ensure their networks and data remain secure.