Businesses today are collecting more and more data as per the requirement of each department. However, the difficulty lies in analysing this wealth of data, understanding the key insights and then securing that data. So, while an enterprise IT department and data scientists are applying an arsenal of data analysis techniques on the massive data collected, they also need to ensure there is no opportunity for data leakage.
1. Safeguard Distributed Programming frameworks
Distributed programming frameworks like Hadoop make up a massive part of modern Big Data distribution. But they come with serious issues of data leakage. They also have “untrusted mappers” or data from multiple sources that may produce error-ridden results. Cloud Security Alliance (CSA) strongly recommends organizations use methods like Kerberos Authentication while ensuring adherence to security policies. Then de-identify the data by separating all Personally Identifiable Information (PII) from the data to ensure personal privacy. After that, authorize access to files and ensure that entrusted code doesn’t leak information.
2. Secure your Non-Relational Data
Non-relational databases such as NoSQL are very common, but they are vulnerable to attacks such as NoSQL injection. To avoid this, we recommend you start by encrypting or hashing passwords, and make sure you do complete encryption by using algorithms like Advanced Encryption Standard (AES), RSA and Secure Hash Algorithm 2 (SHA-256).
3. Secure Data storage and transaction Logs
Storage management plays a significant role in the Big Data security equation. The CSA recommends using signed message digests to provide a digital identifier for each document and to use a technology called SUNDR (Secure Untrusted Data Repository). This helps in detecting unauthorised file modifications by malicious server agents.
4. Endpoint filtering and validation
Endpoint security is important and can be used with trusted certificates, doing resource testing, and connecting trusted devices to your network by using mobile device management. Once it is done, use statistical similarity detection techniques and outlier detection techniques to filter malicious inputs to protect against Sybil and ID-spoofing.
5. Real-time Compliance and Security Monitoring
Compliance is always a headache for firms dealing with a constant deluge of data. It’s best to solve it at the beginning and organisations apply Big Data analytics by using secured tools such as Kerberos, secure shell (SSH) and internet protocol security (IPsec) to make things smoother.