How to protect Big Data? The main Big Data security challenges
About Big Data concept
Big Data is about huge amounts of heterogeneous and fast incoming digital information that can’t be processed with traditional tools. It is characterized by 3V – Volume (the amount of data), Variety (the number of data types), and Velocity (data processing speed).
As now the data amount being collected continues to rapidly grow, the necessity in Big Data highly increases. Just recently only such organizations as government agencies, large enterprises and corporations could afford infrastructure for data storage and analysis.
Today, as technologies become more accessible, Big Data uncovers more benefits, more use cases, and more industries to be applied in.
Big Data software can provide companies with a huge competitive advantage, enabling them to receive in-depth customer data, improve personalization, marketing and CEM (customer experience management), define and eliminate corporate inefficiencies, and much more.
The results may be as follow: better company performance, risk and error minimization, and increased sales.
However, when dealing with Big Data, businesses face many challenges that include capturing large data amounts from various sources, data secure storage, smart analysis, and visualization. And here Big Data security takes the center stage.
Big Data security challenges
Big Data vulnerabilities are defined by the variety of sources and formats of data, large data amounts, a streaming data collection nature, and the need to transfer data between distributed cloud infrastructures.
In other words, the very attributes that actually determine Big Data concept are the factors that affect data vulnerability.
There are various Big Data security challenges companies have to solve. Data leaks, cyber attacks, information use for not legitimate purposes, and many others. Large data sets, including financial and private data, are a tempting goal for cyber attackers.
The consequences of data repository breach can be damaging for the affected institutions. Imagine how a company may suffer in the result of stealing trade secrets, user personal information, customer and employee data!
And the higher the value of data is, the more devastating the effect. So, banking and financial organizations, government entities, and healthcare providers are the first who should pay special attention to Big Data security.
Thus, companies need to focus on the encryption of large data volumes, prevention of data leaks, and protection of corporate information assets. Meanwhile, Big Data security solutions shouldn’t affect the system’s performance and lead to delays. So, how to effectively protect Big Data and minimize vulnerabilities?
How to protect Big Data?
1. Secure tools and technologies
One of the main Big Data security challenges is that while creating most Big Data programming tools, developers didn’t focus on security issues. These tools even include a Hadoop framework and NoSQL databases.
For instance, at the beginning, Hadoop didn’t authenticate users and services. What’s more, it didn’t encrypt data, transmitted between nodes. Understanding the risks and vulnerabilities, developers work on Big Data tools improvement.
So, when starting a Big Data project, take security in mind. Use secure technologies and versions of open-source software, for example, a Hadoop 20.20x version, Cloudera Sentry or Apache Accumulo that will help you protect Big Data.
It should be mentioned there that tools like Cloudera Sentry and Apache Accumulo support a role-based access missing in NoSQL.
2. Account management
Monitor and manage Big Data user accounts: deactivate inactive and dead accounts, require strong reliable passwords by establishing password creation rules. Thanks to controlling and monitoring account access you will reduce the possibility of a successful inside compromise.
3. Access control
Access control involves the two main parts: restricting user access and granting access rights. The challenge here is to build and execute a policy that would correctly distribute rights in each particular scenario.
For this purpose, companies should normalize the changing elements and denormalize the immutable ones, maintain access labels, and use a single user identification (SSO) as well.
Also, monitor administration data, follow the secrecy requirements and make sure of their correct implementation.
4. Protection of data warehouses and transaction logs
Data storage management is a key part of Big Data security issue. For that organizations should use digests of certified messages to ensure a digital identification of each file or document.
ALso, they should use the SUNDR repository technique to detect unauthorized file modifications made by malicious server agents. There a plenty of other techniques, including “lazy” revocation and rotation of keys, encryption schemes based on security policy, and digital rights management (DRM).
However, there is no full-fledged alternative to creating your own secure cloud storage system based on the existing infrastructure.
5. Secure configurations for hardware and software
Develop and integrate servers based on secure images for all systems in your enterprise Big Data architecture. Use proven automation frameworks to accelerate the system configuration and provide Big Data servers’ security and protection. Also, make sure that only a limited audience has access to administrative privileges.
6. Non-relational data protection
Non-relational databases (like NoSQL) are widely used, but they are vulnerable to cyber attacks. For example, NoSQL databases don’t support security features like role-based access control, enabled by traditional ones.
Start with the password encryption and hashing. Ensure that end-to-end data encryption is performed using such algorithms according to Advanced Encryption Standard (AES), RSA, and Secure Hash Algorithm 2 (SHA-256) algorithms.
In addition to these basic measures, add data tagging and security at the object level. Also, you can protect non-relational data by using so-called plug-in authentication modules (PAMs), a flexible method for user authentication.
Hope, the article was useful for you. If you have some questions or Big Data project idea, feel free to apply to us to get a free consultation.