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Big Data Security Management: Tools, Privacy, Best Practices, Issues and Solutions

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Overview of Big Data Security

It is expected that data volumes will rise ever higher in the future. A study from IBM predicted that there would be as many as 2.72 million data scientist workers ready to support companies cope with this amount of data, which is proven accurate. The increased usage of big data would affect the understanding and application of business intelligence and its security by Organizations. Security is a significant concern these days, independent of fields and technologies. As compared to other areas Big Data to have securities issues and attacks happening every single minute, these attacks can be on different components of Big Data, like on stored data or the data source. So what is Big data security?   It's the collective term for all the measures and tools used to guard both the data and analytics methods from attacks, theft, or other malicious activities that could cause a problem or negatively affect them. Like other forms of attacks, big data can be compromised either by attacks originated from online or offline spheres.

Big Data Security Tools

Big Data security should meet four critical criteria – perimeter security and authentication framework, authorization and access, data privacy, and audit and reporting. Authentication –  Required for guarding access to the system, its data, and services. Authentication makes sure the user is who he claims to be. Two levels of Authentication needs to be in place – perimeter and intra-cluster -  Knox Kerberos. Authorization -  Required to manage access and control over data, resources, and services. Authorization can be enforced at varying levels of granularity and in compliance with existing enterprise security standards.   Centralized Administration and Audit It is required to maintain and report activity on the system. Auditing is necessary for managing security compliance and other requirements like security forensics. -  Ranger Data at rest/in-motion Encryption   It is required for control of unauthorized access to sensitive data either while at rest or in motion. Data protection should is considered at the field, file, and network-level, and appropriate methods should be adopted for the security – - HDFS and Wire encryption.

Why is Big Data Security important?

Today almost every organization is thinking of adopting Big Data as they see the potential and utilizing the power of Big Data; they are using Hadoop to process these large data sets. And securing your data is the most important step they are concern about; independent of organization sizes, everyone is trying to secure their data. As a Hadoop process, multiple types of data are combined and stored in a Hadoop data lake, and then the stored information is processed accordingly. As it saves a different kind of data from various sources, so we need to make security essential as almost every enterprise that are using big data has some form of sensitive data, which needs to be protected. Sensitive data can be the user’s credit card details, banking details, passwords. Big data is not a small thing, and we can’t describe it in the context of size, as the size is one of the main features of Big Data. To secure it, someone can construct various strategies like keeping out unauthorised users and intrusions with firewalls, making user authentication reliable, giving training to end-user training, and many others.

What are the Challenges in Securing Big Data?

Below are some common challenges –
  1. Vulnerability to fake data generation
  2. Struggles of granular access control
  3. Often “points of entry and exit’ are secured, but data security inside your system is not secure.
  4. Data Provenance
  5. Securing and protecting data in real-time
Every company has big data in its future, and every company will eventually be in the data business. Author, Thomas H. Davenport

Big Data Security Best Practices

Today, large and varied data are collected and stored than ever, making big data a solution to every industry’s need. And securing big data is not such an easy task, but we can formulate some best practices to secure it; below are some best practices for big data security – As discussed above, the flow of big data comprises three layers: Incoming, stored, and outgoing data. So we can construct our security accordingly and make it more secure.
  1. Continuously monitoring and auditing all access to sensitive data(firstly, find out all the sensitive data).
  2. Alert and react to intrusions and unlawful actions in real-time.
  3. Use the Latest Antivirus Protection.
  4. Secure Data Storage, we can implement a technique called secure untrusted data repository (SUNDR).
  5. Reliable hardware and software configurations.
  6. Implement the principle of least privilege (PoPL).
  7. Implement the data-in-transit and at-rest encryption throughout the Hadoop clusters.
  8. Enable encryption for NoSQL database and all entry points.
  9. Implement intrusion protection systems and intrusion detection systems.
  10. Safeguard data by data encryption while at rest
  11. Fulfill appropriate logging mechanisms
  12. Use proper federation of authorization space.
Hadoop File System (HDFS) is a distributed file system. All types of files can be stored in the Hadoop file system. Taken from Article, Introduction to Data Serialization in Apache Hadoop
Strategies for Granular Access Control, some are listed below –
  1. Point out mutable elements and immutable elements.
  2. Access labels should be maintained, track admin data too.
  3. Use single sign-on, and maintain proper labeling scheme.
  4. Perform audit layer/orchestrator

Compressive Approach to Big Data Security

Due to advanced technology adoption, enterprises are concerned about ensuring their big data approach. Hence, enterprises are looking for avenues for Big Data assurance. To implement a holistic approach for Big Data security, please go through with below steps:

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