With the plethora of devices connecting to the Internet, companies are collecting information from consumers continuously. Even though companies’ data storage capacity is growing daily at enormous rates, there are inadequate solutions for security issues to protect that data.
The big data that companies store and process is very sensitive. This data is subject to the same compliance mandates and requires the same protection against breaches as with traditional databases.
According to a 2016 report by the Identity Theft Resource Center, there is a 40% increase in data breaches. Therefore, companies need to be pro-active when it comes to data security and recognize advanced persistent threats.
Secure Your NoSQL Data
When trying to manage big unstructured data, many companies will move from using a traditional relational database to a NoSQL (Not Only Structured Query Language) database. Although there are benefits of NOSQL, many companies are not aware that they are vulnerable to attacks.
If you are considering moving to NoSQL, there are some things you can do to secure your NoSQL data. First, you should encrypt passwords and data using advance encryption standard (AES). Secondly, unsure only authorized users can access the encrypted data by applying SSL encryption for connecting to the server.
Finally, monitoring logs in real-time will help you detect anything out of the ordinary, while data tagging and enforced time stamps will help prevent any unauthorized access to your big data.
Secure Data Storage
One of the most important parts of securing big data is managing storage. One way is to use auto-tiering. This is a type of storage where items are automatically stored in levels based on the company’s policies.
However, auto tiering does have weaknesses when it comes to security. This is because storage services are not verified and security policies are often mismatched. Furthermore, auto-tiering also keeps a record of all its activities and also needs to be protected to keep the data safe.
If you are going to use auto-tiering, then you should use secure untrusted data repository (SUNDR). This network file system checks for consistencies in the data fork and allows you to store your data securely on untrusted servers. SUNDR can also detect if a file was modified without authorization by malicious server agents.
Also, to detect rollback attacks, you should implement Persistent Authenticated Dictionaries (PADs) which allow you to perform queries against older versions of the data structure.
The foremost challenge facing big data is whether the data is valid from the point of input. If a hacker sets up multiple IDs and feeds false data into the collection system, either using an ID clone attack or Sybil attack, then the data will be invalid.
Since both data collection devices and programs are prone to attacks, your company needs to do all it can to protect its data. You can prevent hackers from tampering with your data by implementing a detection and filter solution. One such solution is to implement endpoint security.
Endpoint security ensures any devices connected to your network follow a definite level of compliance and standards by monitoring the status, activities, software, authorization and authentication of those devices.
Identify Inside Threats
All companies need to be aware that they are vulnerable to not only outside threats, but also to threats within the organization. If employees are dissatisfied, they can sometimes cause intentional harm by stealing information or compromising data.
Sometimes employees are satisfied but are careless when using technology. With the prevalence of cloud computing, data can be accessed from anywhere. Uneducated employees accessing sensitive data via unsecured public Wi-Fi networks can compromise its safety.
Whatever the cause of inside threats is, companies need to be pro-active in identifying risks and managing them. One way is for companies to implement access controls to their systems. Access controls requires encrypted authentication and validation to verify that users are who they say they are as well as control what the users can access.
Finally, to protect big data, your company can implement real-time monitoring. This security feature can warn the company at the initial sign of an attack. However, keeping track of all the real-time data coming from the security information and event management (SIEM) systems requires ample resources.
The solution is to use big data analytics which can help you identify threats and recognize false positives. Big data analytics will also allow you to mine logs to find anomalies. By improving your analytics, you can separate out all the false positives to preserve your data security.
It is more important today than ever for companies to take the necessary steps to protect their big data. Whether security threats are from within or outside of an organization, companies need to be pro-active to recognize threats and protect sensitive data. Implementing controls such as securing your NoSQL data, securing data storage, endpoint validation, identifying inside threats and real-time monitoring will give your company the best chances of staying ahead of threats and keeping its data secure.