When talking about big data from strictly a security standpoint, you have to look at 2 main issues. The first is securing the company and information held by that company in regards to its customers. The second is how to properly use big data techniques to break down and analyze security risks before they become exploited.
Securing Big Data
When talking about big data from strictly a security standpoint, you have to look at 2 main issues. The first is securing the company and information held by that company in regards to its customers. The second is how to properly use big data techniques to break down and analyze security risks before they become exploited.
Securing Big Data
Most businesses are already using Big Data for research and marketing practices. Unfortunately, some of these businesses do not understand the fundamentals, namely, security. As with every other piece of new technology that gets developed and released, security is a simple afterthought, which is a major problem.
As more companies begin to use advanced technologies the more emphasis needs to be put on online business security. Otherwise, we will Big Data breaches grow out of control and cause serious legal consequences for many businesses, as well as significant damage to their reputations.
One major Big Data issue is a lack of organization. As business owners start to find additional ways to classify the information obtained from customers, advanced security measures can be implemented to help protect sensitive data.
Deploying Security Measures For Big Data
The output requirements needed to manage the overhead of traditional SIEM systems, on top of logging systems, are too much for many IT departments. Therefore Big Data is often viewed as some sort of saving grace, as it can be used to help with fraud detection and other major concerns. Big Data alone is not the answer, however. You have to be able to protect your systems and information at all times. This is where an intrusion detection system (IDS) like Snort come into play. By tracking down weakness in your system and watching for suspicious activity, IDS systems can help bolster your security defenses and fix problems before they become advanced security risks.
Actively monitoring the logs of the system from multiple sources on a consolidated scale, Big Data, and IDS systems can pinpoint areas that are weak to intrusion and insider attacks. This means business owners can provide significantly increased detection practices in place to prevent, detect, isolate and stop fraudulent criminal activities.
Risks and Technologies
Like any new technology or practice, there are going to be risks. These risks need to be identified and understand. Once you understand these risks, steps can be taken to prevent or overcome them. Here are a couple of risks you should be aware of.
- Open Source Code – Big Data implementations usually include some type of open source code. This allows for previously unrecognized backdoors into the system.
- New and Unknown – Big Data is a new concept for many business owners and as such, businesses are not aware of additional vulnerabilities they are introducing into their system.
- Authentication Issues – User authentication and data access from various locations might have problems being properly monitored and controlled.
- Regulatory requirements – It is easy for uneducated business owners to fail to fulfill these requires. Properly educate yourself and your staff prior to diving head first into Big Data solutions.
- Constant Monitoring – The surface of nodes within a cluster may not have been properly reviewed and leaves soft spots in your network for intrusions.
If you take the time to research what Big Data is, you’re inevitably going to learn about Hadoop. Hadoop is designed to process extremely large amounts of data quickly, regardless of its structure.
Hadoop uses the MapReduce framework provided by Google to address the problem of generating web search indexes and logs. MapReduce delegates the information over multiple nodes, eliminating the problem of data files being too large to be stored and processed on a single machine. Merging this technology with current Linux servers provides a cost-effective solution to massive computing arrays.
A Hadoop Distributed File System allows servers in a cluster to fail and not abort the computation process. This is done by ensuring the data is replicated across the cluster. An HDFS has no restrictions on the data it can store.
Specialist Skills
Big Data is about processing techniques and outputs then it is about the size of the data itself. This means specific skill sets are needed to use Big Data properly. The specialists for Big Data analysis are in short supply these days, especially when it comes to using some of the older technology platforms.
As Hadoop related technologies continue to grow, the demand for a business employ an IT staff with these very specific skills is paramount. Experts in data mining, predictive modeling, content analysis, text analysis, multivariate statistical analysis and social network analysis are all in extremely high demand. These scientists and analysts work with both unstructured and structured data to bring new insights and intelligence about businesses. Platform management professionals, also in high demand, are needed to launch Hadoop clusters, as well as manage, secure and optimize them.
Before you decide to jump into Big Data, make sure you have a clear picture of what you expect and have the personnel and other resources in place beforehand. By having a clear plan and goals, you can help expand the boundaries of information security for your business. The more you know about Big Data, the scarier it actually may seem. The important thing to remember here is that Big Data can be controlled. Just like fire, which has the power to consume and destroy and commands our respect. Big Data is exactly the same. Before you can harness its power to improve your business, you have to know what it can and cannot do. You must learn how to properly use it, otherwise, it can damage your company more than it can help.
