In today’s world, businesses are collecting more information than ever before. Much of this data is now coming from a variety of connected devices via the Internet of Things. Even though our ability to collect, store and process data has grown exponentially in the past decade, our solutions to ensure this data stays secure are still imperfect.
Some of the data companies store and process is sensitive. Big data deployments are subject to the same regulations and require the same kind of protection against potential breaches as do traditional databases and their related IT infrastructure. For companies taking advantage of the power of big data, this means that everyone from the CIO on down needs to be aware of the relevant security issues and be willing to implement effective solutions to prevent breaches and leaks.
Keep Your Stored Data Secure
When it comes to overall big data security, proper storage management is an essential component. As big data systems deal with petabytes of information, they have to use auto-tiering storage, which automatically assigns a level of storage to different items. While this method may be practical when dealing with huge amounts of data on a daily basis, it does have its share of vulnerabilities, mainly mismatched security policies and the use of unverified storage services.
Another issue with auto-tiering is that it generates logs of every activity and these logs contain information that could be useful to an attacker, which is why they should be protected. One way to mitigate the security issues associated with auto-tiering storage is to use a secure untrusted data repository (SUNDR). This technique detects any unauthorized changes to files by malicious server agents. As SUNDR is a network file system, it lets you safely store data across various untrusted servers by running checks on the data fork’s consistency.
Protect Your Non-Relational Data
It has become quite common for organizations to move from using a standard relational database to a NoSQL (Not Only Structured Query Language) database in order to better handle the large amount of unstructured data they have to process. Even though NoSQL databases have many advantages, they remain vulnerable to different kinds of malicious attacks, including NoSQL injection.
Any organization using NoSQL databases should deploy strategies that minimize the associated security risks. This includes encrypting or hashing passwords, as well as encrypting all data at rest using highly secure algorithms, such as RSA, AES or SHA-256. As for data in transit, it can be protected by using SSL encryption to secure all exchanges of information between the server and clients, thus preventing data from being intercepted or tampered with along the way.
Protect Your Endpoints
When dealing with big data, endpoint security is crucial. Your organization can start implementing it by doing resource testing, using trusted certificates and deploying a mobile device management (MDM) solution to ensure only trusted devices are connected to your network. As big data means dealing with a huge quantity of data sources like endpoint collection devices, validating data at the point of input is a significant challenge.
Both the hardware devices and the software that powers them are susceptible to different kinds of attacks. For example, an infiltrator can use an ID clone attack, or Sybil attack to inject fake data into an organization’s collection system. Endpoint security solutions therefore need to be able to prevent tampering, as well as to detect and filter out any compromised data.
Be Mindful of Inside Threats
Inside threats to an organization’s data can come from disgruntled employees and even more frequently, satisfied employees who are careless about security. Those working more closely with data, such as developers and scientists, should have adequate security knowledge to keep an organization protected. You should ensure that all of your employees are aware of data security best practices. This includes using strong passwords, using antivirus and antimalware software on all devices used to access sensitive information, as well as encrypting any files that need to be sent by email or copied to a mobile data storage device, like an external hard drive or USB stick.
Furthermore, your employees should be aware of the importance of logging off unused computers, the risks of granting data access to other employees or third-party contractors and the risks of accessing an organization’s data through a public Wi-Fi network that uses an unsecured connection. Organizations should routinely monitor their network and minimize the opportunities for malicious users who could deliberately compromise the security of enterprise systems or steal intellectual property.
Monitor Your Big Data Framework
In order to be fully secure, your big data framework needs to include adequate analysis and monitoring tools. Enterprise-grade real-time security monitoring solutions are available and have been specially designed for organizations working with big data. They alert you as soon as a potential attack or other security breach is detected.
However, these SIEM systems provide an enormous amount of data in themselves and few organizations have the resources needed to monitor their feedback at all times and analyze the alerts to separate false alarms from real threats. The solution is to use big data analytics to differentiate between false positives and real intrusion attempts. Analytics tools can mine logs for anomalous behaviors that are indicative of a security breach being attempted, while filtering out the false positives.
A growing number of businesses are now taking advantage of everything big data has to offer. However, handling such huge amount of information comes with its unique security challenges. This is why organizations using big data should deploy strategies that protect their information at the point of collection, while it’s in transit and when it’s stored on their servers, as well as secure their overall infrastructure from potential intruders.Tags: big data, business, security