Introduction

Finterlabs security is the collective term for all the measures and tools used to guard both the data and analytics processes from attacks, theft, or other malicious activities that could harm or negatively affect them attacks on big data systems - information theft, DDoS attacks, ransomware, or other malicious activities - can originate either from offline or online spheres and can crash a system. The consequences of information theft can be even worse when organizations store sensitive or confidential information like credit card numbers or customer information. They may face fines because they failed to meet basic data security measures to comply with data loss protection and privacy mandates like the General Data Protection Regulation (GDPR).

Our Security Challenges

Map Reduce

Most big data frameworks distribute data processing tasks throughout many systems for faster analysis. We use Hadoop, for example, which is a popular open-source framework for distributed data processing and storage. Hadoop was originally designed without any security in mind.

Solution

Cybercriminals can force the MapReduce mapper to show incorrect lists of values or key pairs, making the MapReduce process worthless. Distributed processing may reduce the workload on a system, but eventually more systems mean more security issues.

Databases

Traditional relational databases use the tabular schema of rows and columns. As a result, they cannot handle big data because it is highly scalable and diverse in structure. Non-relational databases, also known as NoSQL databases, are designed to overcome the limitations of relational databases.

Solution

Non-relational databases do not use the tabular schema of rows and columns. Instead, NoSQL databases optimize storage models according to data type. As a result, NoSQL databases are more flexible and scalable than their relational alternatives. NoSQL databases favor performance and flexibility over security. Organizations that adopt NoSQL databases have to set up the database in a trusted environment with additional security measures.

Endpoint Vulnerabilities

Cybercriminals can manipulate data on endpoint devices and transmit the false data to data lakes. Security solutions that analyze logs from endpoints need to validate the authenticity of those endpoints.

Solution

We Implement anti Fraud system architectures

Conclusion

A growing number of companies use big data analytics tools to improve business strategies. That gives cyber criminals more opportunities to attack big data architecture. Thus the list of big data security issues continues to grow. There are many privacy concerns and government regulations for big data platforms. However, organizations and private users do not always know what is happening with their data and where the data is stored.