Big Data Security - Issues, Challenges, Tech & Concerns

Big Data Security - Issues, Challanges, Tech & Concerns

The adoption of big data analytics is rapidly growing. If you don’t get ahead of the curve, there’s big potential for big problems; but if you do plan ahead, there are big opportunities to successfully enable the business.


You might be wondering what the big deal is — and what makes big data special and more challenging. The trouble is that big data analytics platforms are fueled by huge volumes of often sensitive customer, product, partner, patient and other data — which usually have insufficient data security and represent low-hanging fruit for cybercriminals. 


Sensitivities around big data security and privacy are a hurdle that organizations need to overcome. In this paper, we review the current data security in big data and analysis its feasibilities and obstacles. Besides, we also introduced intelligent analytics to enhance security with the proposed security intelligence model.


We must aim to summarize, organize and classify the information available to identify any gaps in current research and suggest areas for scholars and security researchers for further investigation.


What is Big Data Security?

Big data security is the collective term for all the measures and tools used to guard both the data and analytics processes from attacks, theft, or other malicious activities that could harm or negatively affect them. Much like other forms of cyber-security, the big data variant is concerned with attacks that originate either from the online or offline spheres.

Big Data security is the processing of guarding data and analytics processes, both in the cloud and on-premise, from any number of factors that could compromise their confidentiality. 


What makes data big, fundamentally, is that we have far more opportunities to collect it, from far more sources, than ever before. Think of all the billions of devices that are now Internet-capable — smartphones and Internet of Things sensors being only two instances. Now think of all the big data security issues that could generate!


“Big data” emerges from this incredible escalation in the number of IP-equipped endpoints. It is really just the term for all the available data in a given area that a business collects with the goal of finding hidden patterns or trends within it. These, once revealed by analytics tools, can be leveraged to yield an improved outcome down the road (higher customer satisfaction, faster service delivery, more revenue, and so forth).


Unfortunately, many of the tools associated with big data and smart analytics are open source. Often times they are not designed with security in mind as a primary function, leading to yet more big data security issues.


For companies that operate on the cloud, big data security challenges are multi-faceted.


Related Wiki - Big Data - Definition, Importance, Examples & Tools (Big Data Wiki)


These threats are even worse in case of websites which use various vulnerable CMS's such as WordPress include the theft of information stored online, ransomware, XSS Attacks or that could crash a server. Website CMS's are often on the radar of hackers and they exploit it via various kind of hacks. The issue are still worse when companies store information that is sensitive or confidential, such as customer information, credit card numbers, or even simply contact details.



Additionally, attacks on an organization’s big data storage could cause serious financial repercussions such as losses, litigation costs, and fines or sanctions. There are three major big data security best practices or rather challenges which should define how an organization sets up their BI security. Securing big data platforms takes a mix of traditional security tools, newly developed toolsets such as , and intelligent processes for monitoring security throughout the life of the platform.


Enterprises are embracing big data like never before, using powerful analytics to drive decision-making, identify opportunities, and boost performance. But with the massive increase in data usage and consumption comes a whole set of big data security concerns. Ultimately, big data adoption comes down to one question for many enterprises: how can you leverage big data’s potential while effectively mitigating big data security risks?


Big Data Security and Privacy Areas


Big Data Security and Privacy Areas

The future of big data


At this time, an increasing number of businesses are adopting big data environments. The time is ripe to make sure security teams are included in these decisions and deployments, particularly since big data environments — which don’t include comprehensive data protection capabilities — represent low-hanging fruit for hackers since they hold so much potentially valuable sensitive data.


Data security is a detailed, continuous responsibility that needs to become part of business as usual for big data environments. Securing data requires a holistic approach to protect organizations from a complex threat landscape across diverse systems.


The future of big data itself is all but guaranteed to be a bright one — it’s universally recognized these days that smart analytics can be a royal road to business success. So this implies that big data architecture will both become more critical to secure, and more frequently attacked. Thus growing the list of big data security issues…And that, in a nutshell, is the basis of the emerging field of security intelligence, which correlates security info across disparate domains to reach conclusions. The solutions available, already smart, are rapidly going to get smarter in the years to come.

9 Key Big data security issues

Big data is a primary target for hackers. Data security professionals need to take an active role as soon as possible. The reality is that pressure to make quick business decisions can result in security professionals being left out of key decisions or being seen as inhibitors of business growth. However, the risk of lax data protection is well known and documented, and it’s possible to be an enabler rather than an obstacle.


Add in trends like Bring-Your-Own Device (BYOD) and the rise in the use of third-party applications, and big data security issues quickly move to the forefront of top enterprise concerns. A December 2013 article from CSO Online states that many of the big data capabilities that exist today emerged unintentionally, eventually finding their place in the enterprise environment.


Big Data Security Risks Include Applications, Users, Devices, and More


Big data relies heavily on the cloud, but it’s not the cloud alone that creates big data security risks. Applications, particularly third-party applications of unknown pedigree, can easily introduce risks into enterprise networks when their security measures aren’t up to the same standards as established enterprise protocols and data governance policies.


Additionally there’s the issue of users. Particularly in regulated industries, securing privileged user access must be a top priority for enterprises. These are just a few of the many facets of big data security that come into play in the modern enterprise climate.


here’s a shortlist of some of the obvious big data security issues (or available tech) that should be considered.

  1. Distributed frameworks. Most big data implementations actually distribute huge processing jobs across many systems for faster analysis. Hadoop is a well-known instance of open source tech involved in this, and originally had no security of any sort. Distributed processing may mean less data processed by any one system, but it means a lot more systems where security issues can crop up.
  2. Non-relational data stores. Think NoSQL databases, which by themselves usually lack security (which is instead provided, sort of, via middleware).
  3. Storage. In big data architecture, the data is usually stored on multiple tiers, depending on business needs for performance vs. cost. For instance, high-priority “hot” data will usually be stored on flash media. So locking down storage will mean creating a tier-conscious strategy.
  4. Endpoints. Security solutions that draw logs from endpoints will need to validate the authenticity of those endpoints, or the analysis isn’t going to do much good.
  5. Real-time security/compliance tools. These generate a tremendous amount of information; the key is finding a way to ignore the false positives, so human talent can be focused on the true breaches.
  6. Data mining solutions. These are the heart of many big data environments; they find the patterns that suggest business strategies. For that very reason, it’s particularly important to ensure they’re secured against not just external threats, but insiders who abuse network privileges to obtain sensitive information – adding yet another layer of big data security issues.
  7. Access controls. Just as with enterprise IT as a whole, it’s critically important to provide a system in which encrypted authentication/validation verifies that users are who they say they are, and determine who can see what.

    Finally, some specific thoughts on the data itself:

  8. Granular auditing can help determine when missed attacks have occurred, what the consequences were, and what should be done to improve matters in the future. This in itself is a lot of data, and must be enabled and protected to be useful in addressing big data security issues.
  9. Data provenance primarily concerns metadata (data about data), which can be extremely helpful in determining where data came from, who accessed it, or what was done with it. Usually, this kind of data should be analyzed with exceptional speed to minimize the time in which a breach is active. Privileged users engaged in this type of activity must be thoroughly vetted and closely monitored to ensure they don’t become their own big data security issues.


Big Data Security Challenges


There are several challenges to securing big data that can compromise its security. Keep in mind that these challenges are by no means limited to on-premise big data platforms. They also pertain to the cloud. When you host your big data platform in the cloud, take nothing for granted. Work closely with your provider to overcome these same challenges with strong security service level agreements.


Typical Challenges to Securing Big Data:

  • Advanced analytic tools for unstructured big data and nonrelational databases (NoSQL) are newer technologies in active development. It can be difficult for security software and processes to protect these new toolsets.
  • Mature security tools effectively protect data ingress and storage. However, they may not have the same impact on data output from multiple analytics tools to multiple locations.
  • Big data administrators may decide to mine data without permission or notification. Whether the motivation is curiosity or criminal profit, your security tools need to monitor and alert on suspicious access no matter where it comes from.
  • The sheer size of a big data installation, terabytes to petabytes large, is too big for routine security audits. And because most big data platforms are cluster-based, this introduces multiple vulnerabilities across multiple nodes and servers.
  • If the big data owner does not regularly update security for the environment, they are at risk of data loss and exposure.
  • Security tools need to monitor and alert on suspicious malware infection on the system, database or a web CMS such as WordPress, and big data security experts must be proficient in cleanup and know how to remove malware from wordpress.


Big Data Security Technologies


None of these big data security tools are new. What is new is their scalability and the ability to secure multiple types of data in different stages.

  • Encryption: Your encryption tools need to secure data in-transit and at-rest, and they need to do it across massive data volumes. Encryption also needs to operate on many different types of data, both user- and machine-generated. Encryption tools also need to work with different analytics toolsets and their output data, and on common big data storage formats including relational database management systems (RDBMS), non-relational databases like NoSQL, and specialized filesystems such as Hadoop Distributed File System (HDFS).
  • Centralized Key Management: Centralized key management has been a security best practice for many years. It applies just as strongly in big data environments, especially those with wide geographical distribution. Best practices include policy-driven automation, logging, on-demand key delivery, and abstracting key management from key usage.
  • User Access Control: User access control may be the most basic network security tool, but many companies practice minimal control because the management overhead can be so high. This is dangerous enough at the network level, and can be disastrous for the big data platform. Strong user access control requires a policy-based approach that automates access based on user and role-based settings. Policy driven automation manages complex user control levels, such as multiple administrator settings that protect the big data platform against inside attack.
  • Intrusion Detection and Prevention: Intrusion detection and prevention systems are security workhorses. This does not make them any less valuable to the big data platform. Big data’s value and distributed architecture lends itself to intrusion attempts. IPS enables security admins to protect the big data platform from intrusion, and should an intrusion succeed, IDS quarantine the intrusion before it does significant damage.
  • Physical Security: Don’t ignore physical security. Build it in when you deploy your big data platform in your own data center, or carefully do due diligence around your cloud provider’s data center security. Physical security systems can deny data center access to strangers or to staff members who have no business being in sensitive areas. Video surveillance and security logs will do the same.


How Can You Implement Big Data Security?

There are several ways organizations can implement security measures to protect their big data analytics tools. One of the most common security tools is encryption, a relatively simple tool that can go a long way. Encrypted data is useless to external actors such as hackers if they don’t have the key to unlock it. Moreover, encrypting data means that both at input and output, information is completely protected.


Building a strong firewall is another useful big data security tool. Firewalls are effective at filtering traffic that both enters and leaves servers. Organizations can prevent attacks before they happen by creating strong filters that avoid any third parties or unknown data sources.


Data security must complement other security measures such as endpoint security, network security, application security, physical site security and more to create an in-depth approach. By planning ahead and being prepared for the introduction of big data analytics in your organization, you will be able to help your organization meet its objectives securely.

incidents involving data breaches continue to rise rapidly.


This is the reason it’s important to follow the best practices mentioned below for Big Data security:


Boost the security on non-relational data scores

Implement endpoint security

Use customized solutions

Ensure the safety of transaction and data storage logs

Practice real-time security monitoring and compliance

Rely on Big Data cryptography

Start granular audits


Who is responsible for securing big data?


The answer is everyone. IT and InfoSec are responsible for policies, procedures, and security software that effectively protect the big data deployment against malware and unauthorized user access. Compliance officers must work closely with this team to protect compliance, such as automatically stripping credit card numbers from results sent to a quality control team. DBAs should work closely with IT and InfoSec to safeguard their databases.


Secure your big data platform from high threats and low, and it will serve your business well for many years. 


Article (PDF Available)in Communications and Network 09(04):291-301 · January 2017
DOI: 10.4236/cn.2017.94020

Article (PDF Available)in International Journal of Control Theory and Applications 9(43):437-448 · August 2016

Book Article (PDF Available) · April 2019 Edition: 1 Publisher: wphackedhelp

DOI: 40.5534/cn.2019.43020 Cite this publication