Businesses are collecting more data than ever. Much of this data flows in from an increasing number of smart gadgets, all interconnected thanks to the Internet of Things. As computer capabilities grow rapidly, security concerns grow more acute as well, especially when it comes to locally generated data. Businesses, for example, store highly sensitive information and they must adhere to government regulations to afford the necessary protection to consumers. But despite their best efforts, incidents involving data breaches continue to rise rapidly. This is the reason it’s important to follow the best practices mentioned below for Big Data security:
Protect distributed programming frameworks
Hadoop and similar other distributed programming frameworks constitute a large chunk of the modern Big Data network. However, they are highly susceptible to data leakage risks. That’s because these programming frameworks include what is known as “untrusted mappers” or information from various sources capable of producing error-filled aggregated results.
This is the reason why organizations must first establish trust through methods like Kerberos Authentication. At the same time, they must maintain predefined safety policies. Afterward, the data needs to be de-identified by decoupling every shred of PII (personally identifiable information) so that personal privacy remains uncompromised. From there, access to files needs to be authorized with the existing security policy. This way, no untrusted code can leak data via system resources.
Once all the heavy lifting is done, companies can make do with performing regular maintenance to protect against data leakage. Task your IT team with checking worker mappers and nodes in the virtual environment or cloud. Also, instruct them to watch out for altered data duplicates and fake nodes.
Boost the security on non-relational data scores
NoSQL and other non-relational databases generally have minimal security properties. Perform fuzzing methods to test the security and locate vulnerabilities within the NoSQL databases. You can do this by offering unusual, random, or invalid inputs on purpose and testing for them. You might even opt for dumb fuzzing, which relies on random input for detecting vulnerabilities.
Implement endpoint security
When you have trusted certificates at every endpoint, it helps maintain the security of your data. Extra measures opted by your organization like regular resource testing as well as allowing trusted devices to connect to the network via a mobile device management platform also works wonders. However, make sure every piece of data is valid. Remember, there are lots of input applications and devices that are vulnerable to malware and hackers. Intruders are capable of mimicking different login IDs or corrupting the system using fake information. Your Big Data security measures should be able to deter intrusion as well as identify any fake data.
Use customized solutions
Big Data security is a multilayered process. Think of it as a collection of open source frameworks connected to one another for fulfilling a particular requirement. As a result, developing a proper security solution can get complicated, to say the least. With Big Data platforms slowly being treated more like custom apps and less like databases, there is a greater opportunity to use a suitable security approach. The security and data analytics team must understand low-level architecture to make sure all potential threats are taken into account.
Right now, Big Data platforms are quite complex and securing them is tough. A one-size-fits-all solution will not work. To fulfill the complicated security needs of Big Data platforms, companies must customize a collection of tactics that achieve the security objectives recognized at the start of the overall process.
Ensure the safety of transaction and data storage logs
One of the prime aspects of Big Data security is storage management. Use signed message digests so that a digital identifier is present in each digital document or file. Also, opt for the SUNDR (secure untrusted data repository) technique to find unauthorized file modifications attempted by harmful server agents. Other helpful techniques include key rotation and lazy revocation along with digital rights management and policy and broadcast-based encryption strategies. But keep in mind that there’s no alternative to creating your personal secure cloud storage atop the current infrastructure.
Practice real-time security monitoring and compliance
Many companies find compliance bothersome, especially when dealing with a constant stream of data. You need to handle the issue head-on using real-time security and analytics at each level of the stack. Businesses should try applying Big Data analytics through Kerberos, IPsec, SSH, and other tools to better handle real-time data.
After you begin doing that, you’re able to mine the logging events, implement security controls across the stack at the application, cloud, and cluster levels, and even deploy security systems at the front-end, including application-level firewalls and routers. Companies should also avoid evasion attacks that attempt to circumvent the Big Data infrastructure.
Rely on Big Data cryptography
A good Big Data security practice is mathematical cryptography. By creating a system that searches for and filters any encrypted data, like the SSE (searchable symmetric encryption) protocol, enterprises are actually able to run Boolean queries despite the encrypted data. Upon installation, the enterprise should run different cryptographic methods.
Relational encryption is useful for comparing encrypted data without any need to share encryption keys. All you need to do is match identifiers and attribute values. Implementing identity-based encryption is easier for key management in public key settings. In this case, plaintext can be encrypted for a particular identity.
Attribute-based encryption (ABE) is capable of integrating access controls within the encryption scheme. Last, but not the least, you could go for converged encryption, where the encryption keys help the different cloud providers recognize any duplicate data.
Start granular audits
Distinguish between audit data and Big Data to keep the duties separate. While audit data considers information on what’s going on inside the Big Data infrastructure, it needs to be separated from traditional big data. You should consider setting up a different cloud or network segment to host the audit system infrastructure.
Big Data security: Be safe, not sorry
These Big Data security practices will never go out of vogue, and it is in your company’s best interests to implement them as soon as possible to keep all the information safe and secure.
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