Top 5 Big Data Security Issues
Currently, Big Data is like an avalanche that’s quickly making its way down the mountain, getting bigger and faster along the way and most companies are scrambling to keep up. Just like a skier would need the necessary equipment to survive an avalanche such as a mask, helmet gloves etc. Companies also need to be ready for the avalanche that is Big Data and introducing the necessary tools while the avalanche is gaining steam can be difficult, to say the least.
By dismissing Big Data security as a low priority initiative or putting it off, in general, is never a good idea. There is a reason why people always say “Security comes first.” While there are Big Data security and privacy concerns come with their own unique set of challenges, it is an extra reason to become aware of what such challenges are. It may come as a surprise that almost all Big Data security problems are due to the fact that it is big. It’s huge, actually.
Before we get down into the nuts and bolts of Big Data security issues, let’s first take a look at its definition.
What is Big Data Security?
Big data security is a general term used to describe all instruments and methods of guarding the data and analytics processes from attacks, being stolen or other foul play activities that could have a negative impact. Similar to other types of cybersecurity, Big Data attacks could either come from online or offline threats.
If you operate in the cloud, concerns stemming from securing Big Data are even greater. Such threats include theft of information stored online, ransomware, or DDoS attacks could crash your server. If you store sensitive or classified information i.e. customer data, credit card numbers, or even simply contact details, a security breach could cost you even more. An attack on your Big Data storage could result in severe financial consequences such as monetary losses, court costs, fines or sanctions.
Now, let’s take a look at security concerns related to big data.
False Data Production
Prior to moving on to the many operational security problems posed by Big Data, it is worth mentioning false data production. Cybercriminals will try to create some false data and pour it into a data lake in a deliberate attempt to decrease the quality of the data. For example, if you are a manufacturing company that uses sensors to identify production process gone awry, cybercriminals can infiltrate your system and make you sensors show false results such as incorrect temperatures. It is easy not to notice such red flags and fix any security issues before a lot of damage occurs, therefore you should apply several fraud detection methods.
After your data is gathered, parallel processing occurs via a method called MapReduce. The data is divided in half and a mapper processes both halves and allots everything to storage locations. If your mapping code has been compromised, the settings of your current mappers could be changed or added totally foreign ones, this completely ruining your entire data process. Online criminals could force mappers to show incorrect lists of value or key pairs and the results of the MapReduce process will be worthless.
Surprisingly, obtaining access to the MapReduce code is not that difficult since there is no added security measure to protect the information and depend on perimeter security making your information easy pickings for cybercriminals.
Cryptographic Protection Issues
We all know that encryption is a good way to protect sensitive data, nevertheless, it still makes our list. Even though it is possible and recommended to encrypt all information, security protocols are often overlooked. Classified information is often stored on the cloud with no encryption whatsoever because it takes a lot of time to constantly encrypt and decrypt large amounts of data, thus taking away the biggest advantage of Big Data: speed.
Mining Classified Information
Big Data is usually protected by perimeter security which implies securing entrance and exit locations. However, what is being done inside the system itself is unknown.
Without such controls, crooked IT professionals and rival companies can mine your loosely protected data and sell it for their own gain. If such a data leak is regarding the release of a new product or service, finances or the personal information of users, you could incur a lot of losses.
If the data could be better protected by adding security layers, consider adding anonymization as an extra security layer. If a cybercriminal gets a hold of personal information that does not contain any names, phone numbers or addresses, no harm can be done.
Granular Access Controls
There are situations when some of the data becomes restricted and almost no one can see what information is contained inside such as a medical record that contains the patient’s personal information would be restricted. However, there could still be some parts of it which could be useful to people who do not have clearance to access it, such as medical researchers. Since all of the useful information is not available to them, granular access comes into play, meaning that certain individuals can access certain information, but are restricted in terms of what they can see.
In terms of Big Data, it could be hard to grant such access because the technologies themselves were not designed this way. As a way around this, the parts of necessary information sections, are copied to a different data warehouse and delivered to new users as something totally new. If we continue our example of medical research started above, anonymous medical information would be included in the data warehouse, even though the size of the data increases even faster with such a method.
Do not be intimidated by all of these threats since they can all be handled with the necessary tools and processes in place.
Even though there are many threats and concerns, even critical ones, in no way does it imply that you forget or dismiss Big Data. It is necessary to carefully plan out your Big Data adoption keeping in mind all of the security procedures as a top priority. It could be difficult, but there are Big Data consultants with software development solutions standing by.