Big Data security is one of the important concerns in the cloud. Let’s discuss the best practices that could help with big data protection in the cloud.
The most critical source of competitive advantage for modern enterprises is evident in data. As the world of business grows, the snowball of data is rolling up and amassing information from all data points possible. Therefore, we have big data as one of the rapidly evolving phenomena in the world of computing.
Now, while many enterprises are leveraging big data for exceptional business benefits, they tend to ignore the value of big data protection. Almost everyone must have come across the term ‘big data’ in the modern IT landscape at some point in time. With the large-scale adoption of cloud technologies, the massive repository of big data continues to increase every day.
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However, it is very difficult to come across discussions that focus on the protection of big data and the challenges for big data security. There is a limited discourse on the prominent security risks for big data, thereby creating ambiguities regarding the remedial measures for addressing them.
The following discussion takes a dive into information regarding the notable big data security issues in 2020. Readers can find a brief outline of big data and the reason to emphasize on its security. Then, the outline of prominent security risks for big data would help readers find the various vulnerabilities of big data. Furthermore, the discussion would also highlight information on best practices for safeguarding big data.
Reasons for the Vulnerability of Big Data
Many readers who have just started getting the air of ‘big data’ must be confused as to what’s so big about data! As a matter of fact, data is the same, but the sources available for collecting data are more than ever before. Billions of devices connected to the internet are prominent examples of points that generate data on a massive scale continuously.
The rapid increase in the number of internet-connected endpoints is also driving an exponential boom in the generation of big data. Businesses collect data from a particular area to find out hidden patterns and insights. The value of big data protection is evident in the value it generates for business in various areas such as faster service delivery, additional revenue, or improved customer satisfaction. So, where does the problem of security arises with big data?
The basic point of threat for big data security arises from the infrastructure implemented for big data storage. The architecture of big data storage systems presents vulnerabilities for criminal activity and malware attacks. In addition, the majority of tools related to big data and smart analytics are open source in nature.
Generally, these tools don’t consider security as a primary function, thereby leading to higher possibilities of big data security issues. Data being a key resource for enterprises in the modern business landscape can lead to dangerous consequences upon facing compromises. Therefore, it is important to look out for the possible threats to big data for ensuring the best possible big data protection measures.
Prominent Threats to Big Data Security
The significance of big data is not something that anyone can ignore easily, especially after reflecting on the root causes of big data security challenges. So, the focus of our discussion should now turn towards the identification of notable issues that challenge the security of big data. Here are the notable challenges that enterprises will face for big data protection in 2020 and beyond.
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Fake Data
The most formidable threat for big data security in the present times is fake data generation. Compromises in access to databases can allow hackers to create fake data and place it in the centralized repository. Now, you have to remember that the centralized repository allows highly scalable storage for all structured and unstructured data for enterprises. As a matter of fact, the facility of cloud storage for big data is one of the notable drivers of cloud transformation among enterprises worldwide.
Therefore, businesses that depend on real-time data analytics and the Internet of Things (IoT) have to face a dual-threat. On the one hand, they have to manage restrictions on access while also establishing the capabilities for detecting fake data generation. For example, manufacturing companies could identify false temperature reports as notable causes for slowing down production and incurring a considerable loss of revenue.
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Granular Access Control
Access control is the root cause of one of the prominent big data security challenges enterprises face currently. Big data environments rely on granular access control as one of the primary functionalities. Granular access control allows the allocation of different levels of access to an enterprise’s database and dashboard.
Therefore, the security of big data is a prominent objective of granular access control. However, enterprises don’t take the increase in a number of data sets and the complexity of databases into account for determining granular access control. For example, if only certain personnel in an organization have access to a specific data set, then there will be a significant delay in identifying a breach.
In addition, granular access also imposes restrictions on the information that users can see in a particular data set. As a result, one of the important tools for protecting big data is actually the reason for a prominent threat. How? The restrictions in granular access control create considerable issues for the maintenance and performance of the system.
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Distributed Frameworks
One of the basic threats for big data protection arises from the distributed frameworks. Enterprises that implement big data would have to ensure the distribution of data analytics across different systems. For example, one of the notable big data analytics tools, Hadoop, has a highly scalable and distributed computing design for a big data environment.
However, the concern of efficient security in distributed frameworks is one of the critical challenges for big data security. Experts have stated that the discrepancy between the time of attack and time of response continues to be a challenge. In worst-case scenarios, attacks could act within minutes while the recovery may take months.
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Lack of Security Audits
Security audits are the need of the hour for big data protection, yet there are very few! Best practice would indicate the integration of security audits into any system development life cycle, especially with respect to big data. Most important of all, a security audit is an often-overlooked factor in the majority of cases when working with big data. The challenge is further complicated due to the lack of qualified employees for the design and implementation of reliable security audits.
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Real-Time Security Compliance Issues
Enterprises have not been shy of utilizing the optimal power of the cloud, and real-time big data analytics have become an instant favorite across many enterprises worldwide. Real-time data analytics offer enterprises a competitive advantage, albeit creating the necessity for the implementation of security compliance tools specifically for real-time analytics.
However, big data protection in the cloud faces a considerable threat due to the complications in the implementation of security compliance tools and the creation of massive volumes of data simultaneously. In addition, false positives have also become an increasingly troubling issue for real-time analytics. Therefore, maintaining real-time security compliance can turn into a tiresome exercise with concerns of unnecessary wastage of resources.
Best Practices for Big Data Protection in Cloud
Ideally, the first course of action in big data protection should focus on addressing all the above-mentioned threats comprehensively. However, it is impossible to anticipate all the possible threats in advance and create the perfect security safeguard for big data. Therefore, enterprises should follow the essential best practices to find reliable answers to threats for big data protection in the cloud. Here are some of the critical pointers for organizations to address concerns of big data security effectively.
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Addressing the Vulnerabilities of Distributed Frameworks
Hadoop is one of the first in line among distributed frameworks that brought big data to the forefront. The modern big data landscape comprises of Hadoop and many other similar distributed programming networks. However, as you might have noticed above, distributed frameworks are highly vulnerable to big data security risks.
The presence of information from different sources can result in issues of generating error-filled aggregated results. So, the best practice for big data protection should align with establishing trust through methods such as Kerberos Authentication. In addition, it is important to establish strict safety policies.
Then, decoupling all personally identifiable information (PII) to the minute level could ensure promising protection for personal privacy. The privacy of data on distributed frameworks should also involve the use of existing security policy for authorizing access to files. As a result, organizations can have safeguards against untrusted code that can leak data through system resources.
Now, enterprises might have found the answer to “how to protect privacy in big data”. However, best practice indicates the emphasis on regular maintenance for ensuring the best safeguards against data leakage. The IT team of enterprises should always be on the lookout for worker mappers and nodes in the cloud or virtual environment. Furthermore, it is highly important that they look for modified data duplicates or fake nodes.
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Strengthen the Security of Big Data Storage
Storage of data is one of the notable setbacks for big data protection as you may have noticed already. The minimal security functionalities in NoSQL and other non-relational data stores is one of the major concerns of an enterprise that thinks of how to protect privacy in big data. You should implement fuzzing methods for testing the security infrastructure of storage.
It will help you identify the vulnerabilities in the NoSQL databases. Random, unusual or invalid inputs are the best alternatives easily available for testing the integrity of the storage system. Dumb fuzzing, i.e. random inputs for identifying vulnerabilities can help in anticipating the security status of big data storage.
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Focus on Endpoint Security
The next crucial best practice for big data protection refers to the implementation of endpoint security. Establish trusted certificates at different endpoints for ensuring better security of enterprise big data. The organization should follow additional measures such as regular resource testing and enabling the connection of trusted devices to the network through mobile device management platforms.
However, it is highly crucial for organizations to ensure the validity of every piece of data. It is very important to note that many input applications and devices are highly vulnerable to cyber threats. ID replication or system corruption by using fake information can present considerable threats to the integrity of big data. Therefore, the big data protection system of an organization should prevent external attacks as well as to detect fake data.
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Work on Customized Security Strategy
Enterprises have to understand that adoption of big data is a multi-layered process. You can assume it as a collection of open-source frameworks connected with each other for addressing a specific requirement. Therefore, the development of an ideal security solution for big data protection can be difficult. The ideal practice, in this case, would be to adopt customized security solutions.
Security and data analytics departments of an enterprise should understand low-level architecture and consider all potential threats. Organizations have to understand that a single-solution-for-all approach will not work efficiently for safeguarding big data. Therefore, enterprises need a customized security strategy that aligns with their security objectives for safeguarding big data.
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Refining Real-time Security Compliance Approach
Various enterprises are quick to find out troubles with compliance, especially when they are managing a constantly increasing data collection. Enterprises have to opt for real-time security and analytics for each level of the stack. It is highly important for businesses to implement Big Data analytics by utilizing IPsec, Kerberos, SSH, and other tools for efficient management of real-time data.
Mining of logging events, deployment of security systems at the front-end such as application-level firewalls and routers are some of the other recommended best practices for ensuring compliance. Most important of all, enterprises can achieve the deployment of security controls at the application stack, cloud, and cluster levels. Subsequently, enterprises can maintain compliance with security standards while also preventing any evasion attacks.
One who deals with the enterprise data, need to be familiar with data backup and recovery. To learn the basics, read my previous on Data Backup and Recovery.
Final Notes
Now, it is important for enterprises to focus on other notable challenges for big data protection. However, the most critical requirement to deal with security challenges for big data is to maintain your calm. As you can notice from the discussion, try to implement a security strategy right from the beginning. The planning for a security strategy would solve the majority of issues for safeguarding big data.
You can identify the vulnerabilities and then work on the appropriate remedial measures according to your budget. The bright lining for enterprises is the superior capability of smart analytics tools that can support effective planning for better security. As the world of computing grows smart, the solutions for security would also get smarter!