Our News

Defining Big Data

Browse IT websites and IT literature and you will likely read about “big data”. This term is quite popular yet many people really have no idea as to what it means. Those who are concerned with data security issues are especially puzzled as to what big data really means in the context of information protection.

Data is classified as “big” when it is derived from a number of different sources. Nowadays, we have all sorts of ways to collect and organize data. Consider the seemingly endless number of devices that are connected to the web. From smartphones to laptops, desktops, tablets and all sorts of machines connected through the Internet of Things, data is being generated left and right.

Think of big data as a term used to describe all of the data an organization gathers with the aim of uncovering important trends and patterns. Contemporary analytics allows the discovery of these patterns. Once pinpointed, such patterns are used to boost outcomes in the form of enhanced customer experiences, increased revenue, rapid delivery and beyond.

Big Data is a Target
For all the positives provided by big data, there are some negatives that the media tends to neglect in an effort to romanticize big data. Take a moment to ponder how the massive creation of data spawns highly nuanced security challenges. Big data stems from an abundance of IP-equipped points in businesses, homes and beyond. The downside to big data is that the architecture relied upon for the storage of such voluminous information is an attractive target for criminals as well as malware attacks.

Though many commend big data tools for their open source nature, this “access for all” approach also creates vulnerabilities. Plenty of open source big data tools and smart analytics are designed with extensive security weaknesses as there is an over-focus on the ability to collect massive amounts of information. Cyber Security Solutions has identified the top big data security issues that every organization should keep in mind.

1. Real-Time Security
Real-time security and complex compliance programs tend to create an excess of information. If one can determine a way to filter out the false positives, employees can key in on legitimate breaches. This is easier said than done and best left to the security professionals at Cyber Security Solutions.

2. The Issue of Endpoints
Security programs sometimes pull logs from endpoints. These solutions must check the legitimacy of the endpoints. If they don’t, the analysis won’t provide much utility.

3. Storage
Big data is typically stored in a multi-tiered manner. The exact storage manner hinges on cost as well as the organization’s unique performance needs. Flash media is often used for data that is highly valued . As a result, a tier-oriented storage strategy is employed to prevent improper access.

4. Framework Challenges
The implementation of big data typically spreads out gigantic processing projects to an array of systems. This distribution allows for rapid analysis yet it also presents a security challenge in the form of reliance upon an abundance of systems. More systems means a greater chance of a security breach. Add in the fact that open source programs that permit such distribution often lack security and it is easy to see why more and more organizations are leaning on the data protection experts at Cyber Security Solutions.

5. Non-relational Sources of Data

Databases like NoSQL are often woefully short on security protocols.

6. Access Controls
A system that provides encrypted authentication and validation is an absolute must to ensure that users are legitimate. Furthermore, such security ensures that the proper parties are provided access to sensitive information as opposed to low-level employees or outsiders who attempt to breach the system.

7. Data Mining
Data mining efforts are central to the prudent use of big data. These solutions identify important trends that lead to the formulation of business strategies. This is precisely why data mining solutions should be extensively secured against outside threats as well as internal employees who improperly use network privileges to access valuable information.

8. Data Origin
Data provenance is centered on information about the data itself. This style of information helps determine the origins of data, figure out who accessed the data and how it was used. Such data is typically studied in a rapid manner to mitigate the duration of the breach. Anyone with high-level privileges in the context of data access should be comprehensively screened to ensure they can be fully trusted.

9. Granular Auditing
This style of auditing helps in the quest to figure out when overlooked data attacks occur. It also sheds light on the consequences of these missed attacks and how such attacks can be accurately identified (and prevented) across posterity. Such auditing is a significant undertaking that is best left to the professionals at Cyber Security Solutions.

A Look at the Future of Big Data Security
Big data will continue to serve a critically important role for organizations of all types long into the future. There is no question as to whether smart analytics provides a path to success. As a result, big data security issues will be squarely in the spotlight in the years to come. Look for privacy concerns to increase as even larger amounts of data are compiled. Big data attacks will be on the upswing and efforts to safeguard information will be ramped up in response. The government will likely step in with regulations that require specific security protocols.

The bottom line is that big data security weaknesses put organizations, governments and everyday people in harm’s way. Cyber Security Solutions is here to help with all of your big data security issues and any other cyber security challenges facing your organization. Contact us today to schedule a consultation.