Businesses are now collecting and using a huge amount of data. Much of this flows from an increasing range of smart devices, all interconnected as the IoT (Internet of Things). Our computer capacity continues to grow rapidly, but there is still concern over security issues that can compromise even locally generated information. In business, highly sensitive information is stored, and it’s necessary to observe government regulations to protect consumers. At the same time, incidents of data breaches continue to rise. This is why it’s essential to make data protection a priority and establish strict security measures.

Here are five tips that can help you guard data against breaches in both big data deployments and any software accessing the data.

Secure Data Storage

Managing storage is a critical part of any data strategy. Auto-tiering is necessary when you’re looking at petabytes of information. New data is automatically assigned to different storage levels to make managing huge volumes of data simpler. However, this can create other problems due to issues like unverified services or contradictory protocols. Auto-tiering also generates logs of its storage activities which also have to be protected and maintained. SUNDR (secure untrusted data repository) helps with this by monitoring for and detecting unauthorized file operations. These can come from malicious software agents. SUNDR utilizes consistency checks to ensure data is stored securely.

Secure Non-Relational Data

Many organizations handle a large volume of unstructured data such as images. They turn from standard SQL relational databases to NoSQL deployments. These solutions are becoming more common but are still vulnerable to injection attacks where malignant code is inserted. Recommended security measures include hashing or encrypting passwords. You should also use effective end-to-end encryption algorithms such as RSA, AES, and SHA-256, as well as SSL encryption.

Ensure Endpoint Security

Trusted certificates at each endpoint will help to ensure that your data remains secured. Additional measures that your organization should use include regular resource testing and allowing only trusted devices to connect to your network through the use of am MDM (mobile device management) platform.

One challenge lies in ensuring that all data is valid, given the wide scope of devices and data collection technologies. Many input devices and applications are vulnerable to hackers and malware. Intruders can mimic multiple logon IDs or corrupt the system with false data. Your big data solution should be capable of both preventing intrusion and identifying false data.

Prevent Inside Threats

Your company is also exposed to internal security risks, whether from disgruntled or simply careless employees. This is especially challenging in business environments where employees working with the data are not fully educated on proper security practices and behavior, including data scientists and software developers.

It’s important that you provide digital security training to all employees. They should know about password safety, logging off unused computers, granting permissions to other employees and risks of accessing data via public Wi-Fi.

Your company should also have user logs in place to help identify workers who might attempt to steal intellectual property, use stolen logon credentials, or otherwise try to compromise or bypass network security protocols.

Analyze and Monitor

A big data solution that includes tools for both analysis and monitoring in real time can raise alerts the instant network intrusion is detected. But this can result in large amounts of network data. Your goal is to provide an overall picture of what’s happening over sometimes large networks from moment to moment. Your organization may not have the resources to monitor and analyze all the feedback generated, including false alarms as well as real threats.

The solution is that big data analytics itself can be used to improve network protection. Your security logs can be mined for anomalous network connections. This will make it easier for you to identify actual attacks as opposed to false positives going forward.

Final Thoughts

Automated data collection is increasing the exposure of companies to data loss. When considering a big data solution, you can best mitigate the risks through strategies such as employee training and varied encryption techniques. But it’s also crucial to look for solutions where real security data can be analyzed to drive improvements.

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Big Data could not be described just in terms of its size. However, to generate a basic understanding, Big Data are datasets which can’t be processed in conventional database ways to their size. This kind of data accumulation helps improve customer care service in many ways. However, such huge amounts of data can also bring forth many privacy issues, making Big Data Security a prime concern for any organization. Working in the field of data security and privacy, many organizations are acknowledging these threats and taking measures to prevent them.

WHY BIG DATA SECURITY ISSUES ARE SURFACING

Big data is nothing new to large organizations, however, it’s also becoming popular among smaller and medium sized firms due to cost reduction and provided ease to manage data.

Cloud-based storage has facilitated data mining and collection. However, this big data and cloud storage integration has caused a challenge to privacy and security threats.

The reason for such breaches may also be that security applications that are designed to store certain amounts of data cannot the big volumes of data that the aforementioned datasets have. Also, these security technologies are inefficient to manage dynamic data and can control static data only. Therefore, just a regular security check can not detect security patches for continuous streaming data. For this purpose, you need full-time privacy while data streaming and big data analysis.

PROTECTING TRANSACTION LOGS AND DATA

Data stored in a storage medium, such as transaction logs and other sensitive information, may have varying levels, but that’s not enough. For instance, the transfer of data between these levels gives the IT manager insight over the data which is being moved. Data size being continuously increased, the scalability and availability makes auto-tiering necessary for big data storage management. Yet, new challenges are being posed to big data storage as the auto-tiering method doesn’t keep track of data storage location.

VALIDATION AND FILTRATION OF END-POINT INPUTS

End-point devices are the main factors for maintaining big data. Storage, processing and other necessary tasks are performed with the help of input data, which is provided by end-points. Therefore, an organization should make sure to use an authentic and legitimate end-point devices.

SECURING DISTRIBUTED FRAMEWORK CALCULATIONS AND OTHER PROCESSES

Computational security and other digital assets in a distributed framework like MapReduce function of Hadoop, mostly lack security protections. The two main preventions for it are securing the mappers and protecting the data in the presence of an unauthorized mapper.

SECURING AND PROTECTING DATA IN REAL TIME

Due to large amounts of data generation, most  organizations are unable to maintain regular checks. However, it is most beneficial to perform security checks and observation in real time or almost in  real time.

PROTECTING ACCESS CONTROL METHOD COMMUNICATION AND ENCRYPTION  

A secured data storage device is an intelligent step in order to protect the data. Yet, because most often data storage devices are vulnerable, it is necessary to encrypt the access control methods as well.

DATA PROVENANCE

To classify data, it is necessary to be aware of its origin In order to determine the data origin accurately, authentication, validation and access control could be gained.

GRANULAR AUDITING

Analyzing different kinds of logs could be advantageous and this information could be helpful in recognizing any kind of cyber attack or malicious activity. Therefore, regular auditing can be beneficial.

GRANULAR ACCESS CONTROL

Granular access control of big data stores by NoSQL databases or the Hadoop Distributed File System requires a strong authentication process and mandatory access control.

PRIVACY PROTECTION FOR NON-RATIONAL DATA STORES

Data stores such as NoSQL have many security vulnerabilities, which cause privacy threats. A prominent security flaw is that it is unable to encrypt data during the tagging or logging of data or while distributing it into different groups, when it is streamed or collected.

CONCLUSION

Organizations must ensure that all big data bases are immune to security threats and vulnerabilities. During data collection, all the necessary security protections such as real-time management should be fulfilled. Keeping in mind the huge size of big data, organizations should remember the fact that managing such data could be difficult and requires extraordinary efforts. However, taking all these steps would help maintain consumer privacy.

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