Big Data Security Tools and Management Best Practices

Introduction to Big Data Security

Big Data Security is the collective term for all the measures and tools used to guard both the data and analytics methods against attacks, theft, or other malicious activities that could cause a problem or negatively affect them. Like other forms of attacks, it can be compromised either by attacks originating from online or offline spheres.

It is expected that data volumes will rise ever higher in the future. A study from IBM predicted that as many as 2.72 million data scientist workers would be ready to support companies in coping with this amount of data, which is proven accurate. The increased usage of big data would affect organizations' understanding and application of business intelligence and its security. Security is a significant concern these days, independent of fields and technologies.

Big Data is all about large and complex data sets, which can be both structured and unstructured. Click to explore about, What are its Characteristics, Challenges, Tools & Use Cases

Why is it important?

Today's adoption of Big Data is on the rise as organizations recognize its potential and its power. Many organizations are turning to Hadoop to process their large data sets, so data security becomes a top priority. Regardless of the size of the organization, everyone is striving to protect their data. As a part of the Hadoop process, various types of data are consolidated and stored in a Hadoop data lake, which is then processed accordingly.

Since it encompasses different data types from various sources, robust security measures become essential, especially since most enterprises using Big Data handle sensitive information. This sensitive data can include credit card details, banking information, and passwords. The significance of protecting this data cannot be understated, and it goes beyond simply considering its size, as size is just one aspect of it. To ensure its security, organizations can employ various strategies, such as implementing firewalls to keep out unauthorized users and intrusions, establishing reliable user authentication protocols, providing thorough end-user training, and more.

Top 5 Big Data Security Tools in 2023

What is Data Security Management?

Data security management is the process of ensuring that business data is secure and out of the hands of unauthorized individuals. Data security management systems strongly emphasize safeguarding sensitive data, such as private information or crucial intellectual property for businesses. For instance, developing information security policies, identifying security risks, and spotting and evaluating security threats to IT systems are all examples of data security management tasks. Sharing information about data security best practices with employees throughout the organization, such as being cautious when opening email attachments, is another crucial practice.

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Big data security implementation

The basic implementation process to secure any platform contains different stages, as follows:

1. Data Classification

2. Sensitive Data Encryption

3. Data Storage Using ORAM Technology

4. Data Access through Path-Hiding Approach

What are the significant challenges?

Several big data security issues to protecting it can jeopardize its safety. It's important to note that these issues aren't limited to on-premise systems. They even have to do with the cloud. Take nothing for granted when hosting a big data platform in the cloud. With good security service level arrangements, the vendor needs to resolve these same obstacles.

Typical Challenges to Securing Big Data

1. Newer innovations in active growth include advanced computational methods for unstructured and non-relational databases (NoSQL). Protecting these new toolsets for security technologies and procedures can be complicated.
2. Data ingress and storage are well-protected with advanced encryption software. They may not have the same effect on data production from multiple analytics platforms to multiple locations.
3. Administrators of its systems can decide to mine data without obtaining permission or warning. Regardless of whether the motive is interest or criminal benefit, the monitoring software must track and warn about suspicious access.
4. The sheer scale of its installation, which can range from terabytes to petabytes, makes regular security audits impossible. Since most large data platforms are cluster-based, which exposes several nodes and servers to several vulnerabilities.
5. Its owner is at risk of data loss and disclosure if the environment's protection is not updated regularly.
6. Its protection professionals must be proficient in cleanup and know how to delete malware. Security software must detect and alert suspicious malware infections on the system, database, or web.

Top six challenges that come in the way while implementing Big Data. Click to explore about, Big Data Challenges and Solutions

Top 10 best practices for securing big data

The below listed are the top 10 Best Practices for securing it:

1. Safeguard Distributed Programming Frameworks

First, create trust using methods like Kerberos Authentication and ensure predefined security policies are followed. The data is then "de-identified" by decoupling all personally identifiable information (PII) from it, ensuring that personal privacy is not jeopardized.

Then, using mandatory access control (MAC), such as the Sentry tool in Apache HBase, you allow access to files based on a predefined security policy and ensure that untrusted code does not leak information through device resources. After that, the hard part is done; all that is left is maintaining the system to prevent data leakage. In a cloud or virtual environment, the IT department should scan worker nodes and mappers for bogus nodes and altered duplicates of results.

2. Secure Non-Relational Data

Non-relational databases like NoSQL are common but vulnerable to NoSQL injection attacks by encrypting or hashing passwords and maintaining end-to-end encryption using algorithms such as advanced encryption standard (AES), RSA, or Safe Hash Algorithm 2.

With the advent of Big Data, the structured approach fails miserably to cater to the needs of the humongous information processing that tends to be unstructured in nature SQL vs NoSQL vs NewSQL: The Full Comparison

3. Secure Data Storage and Transaction Logs

Storage control is a critical component of its reliability. By using signed message digests to have a cryptographic identifier for each digital file or record and using a technique known as a secure untrusted data repository (SUNDR) to detect unauthorized file modifications by malicious server agents

4. Endpoint Filtering and Validation

Using a mobile device management solution, you can use trusted credentials, perform resource verification, and link only trusted devices to the network. Using statistical similarity detection and outlier detection strategies, you can process malicious inputs while defending against Sybil attacks (one person posing as several identities) and ID-spoofing attacks.

5. Real-Time Compliance and Security Monitoring

Using Big Data analytics, organizations can use techniques like Kerberos, Safe Shell, and internet protocol protection to get a grip on real-time data. It's then simple to monitor logs, set up front-end security mechanisms like routers and server-level firewalls, and start putting security controls in place at the cloud, network, and application levels.

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6. Preserve Data Privacy

Employee awareness training centers on new privacy laws and ensures that information technology is kept up to date by using authorization processes. In addition, data leakage from different databases can be regulated by analyzing and tracking the infrastructure that connects the databases.

7. Big Data Cryptography

Mathematical cryptography has improved significantly. Enterprises can run Boolean queries on encrypted data by creating a method to scan and filter encrypted data, such as the searchable symmetric encryption (SSE) protocol.

8. Granular Access Control

The two main aspects of access management are limiting and allowing user access. The key is creating and executing a policy that automatically selects the best option in any situation.

Graph Databases uses graph architecture for semantic inquiry with nodes, edges, and properties to represent and store data. Role of Graph Databases in Big Data Analytics

To set up granular access controls:

1. Immutable elements should be denormalized, and mutable elements should be normalized.
2. Please keep track of confidentiality provisions to make sure they're followed.
3. Keep track of control marks.
4. Keep track of administrative information.
5. Use a single sign-on (SSO) and a labeling system to ensure proper data federation.

Strategies for Granular Access Control are listed below :

1. Point out mutable elements and immutable elements.
2. Access labels and admin data should be maintained.
3. Use single sign-on and maintain a proper labeling scheme.
4. Perform audit layer/orchestrator.

9. Granular Auditing

Granular auditing is essential for its protection, particularly after a system attack. Organizations should develop a unified audit view following an attack and include a complete audit trail with quick access to the data to reduce incident response time.

The integrity and security of audit records are also important. Audit data should be kept isolated from other data and safeguarded with granular user access controls and routine reporting. When configuring auditing, keep it and audit data separate and allow all necessary logging. An orchestrator tool like ElasticSearch can make it easier to do.

10. Data Provenance

It's provenance metadata that Big Data applications produce. This is a different kind of data that requires special protection. Creating an infrastructure authentication protocol that manages access, sets up daily status alerts, and constantly checks data integrity with checksums.

Hadoop File System (HDFS) is a distributed file system. All types of files can be stored in the Hadoop file system.Know more : Apache Hadoop 3.0 Features and Its Working

Essential Security Measures for Big Data Systems

It should meet four critical criteria – perimeter security and authentication framework, authorization and access, data privacy, and audit and reporting.

1. Authentication

Required to guard access to the system, its data, and services. Authentication makes sure the user is who he claims to be. Two levels of Authentication need to be in place – perimeter and intra-cluster -  Knox,  Kerberos.

2. 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.

3. 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

4. Data at rest/in-motion Encryption

It is required to control unauthorized access to sensitive data while at rest or in motion. Data protection should be considered at the field, file, and network levels, and appropriate methods should be adopted for security - HDFS and wire encryption.

Big data security solutions for Enterprise

The use cases of Big Data Security are described below:

1. Cloud Security and Monitoring 

The communication and data need to be secured on the cloud. It offers cloud application monitoring, security to host sensitive data and support for several relevant cloud platforms.

2. Insider Threat Detection

An insider threat can destroy a network. With the support of big data analytics, threats can be detected and avoided.  

3. User Behaviour Analysis

Tracking and analyzing users' behavior can help monitor unusual behavior to detect suspicious patterns and thus prevent failure in the security of big data.

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What are the Frequently asked questions?

 The frequently asked questions about big data security are the following:

What are big data security and privacy?

It is the collective term for all the measures and tools used to guard data and analytics methods against attacks, theft, or other malicious activities that could cause a problem or negatively affect them.

It is a privacy protection that can manage the volume, variety, velocity, and value of Big Data to minimize risk as it is moved between source and destination, multiple environments, processed, analyzed, and shared.

What is the main purpose of developing a big data security strategy?

With the increasing number of attacks on data, organizations need to have a Big Data Security strategy. The primary goal of its strategy is to protect enterprise data against internal or external attacks. Enterprises should protect their data from ransomware, DDoS, and theft.

What are the security issues in big data?

Below is the list of 11 security issues in it:

1. Data Storage
2. Fake Data Generation

3. Data Quality Issues 

A process that describes task description, time requirements, Deliverables, and pitfalls. Download to explore the potential of Data Warehouse.

Effective big data security Solutions

1. Enabling Control and Agility

For businesses to secure sensitive data across their Big Data environments, including data sources, infrastructure, and analytics, the Data Security Platform provides data discovery and classification, granular access controls, strong encryption, tokenization, and centralized key management. Utilize big data analytics with the knowledge that all collected and mined data, including sensitive data, is secure.

2. Creating a Trusted Platform for Cryptographic Processing

It is crucial to protect sensitive data from compromise with big data encryption because handling exponentially growing volumes of sensitive data carries a high level of risk. Big Data security solutions use the tried-and-true data protection technologies of cryptography and key management to secure Big Data and establish trust for digital assets.

Summing up Security in Handling Vast Data

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