Enterprises are using big data analytics to identify business opportunities, improve performance, and drive decision-making. Many big data tools are open source and not designed with security in mind. The huge increase in data consumption leads to many data security concerns. This article explains how to leverage the potential of big data while mitigating big data security risks.
What Is Big Data Security?
Big data security is an umbrella term that includes all security measures and tools applied to analytics and data processes. 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 be in compliance with data loss protection and privacy mandates like the General Data Protection Regulation (GDPR).
Big Data Security Challenges
Big data challenges are not limited to on-premise platforms. They also affect the cloud. The list below reviews the most common challenges of big data on-premises and in the cloud.
Most big data frameworks distribute data processing tasks throughout many systems for faster analysis. Hadoop, for example, is a popular open-source framework for distributed data processing and storage. Hadoop was originally designed without any security in mind.
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.
Traditional relational databases use 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.
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.
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.
For example, hackers can access manufacturing systems that use sensors to detect malfunctions in the processes. After gaining access, hackers make the sensors show fake results. Challenges like that are usually solved with fraud detection technologies.
Data Mining Solutions
Data mining is the heart of many big data environments. Data mining tools find patterns in unstructured data. The problem is that data often contains personal and financial information. For that reason, companies need to add extra security layers to protect against external and internal threats.
Companies sometimes prefer to restrict access to sensitive data like medical records that include personal information. But people that do not have access permission, such as medical researchers, still need to use this data. The solution in many organizations is to grant granular access. This means that individuals can access and see only the information they need to see.
Big data technologies are not designed for granular access. A solution is to copy required data to a separate big data warehouse. For example, only the medical information is copied for medical research without patient names and addresses.
Addressing Big Data Security Threats
Security tools for big data are not new. They simply have more scalability and the ability to secure many data types. The list below explains common security techniques for big data.
Big data encryption tools need to secure data-at-rest and in-transit across large data volumes. Companies also need to encrypt both user and machine-generated data. As a result, encryption tools have to operate on multiple big data storage formats like NoSQL databases and distributed file systems like Hadoop.
User Access Control
User access control is a basic network security tool. The lack of proper access control measures can be disastrous for big data systems. A robust user control policy has to be based on automated role-based settings and policies. Policy-driven access control protects big data platforms against insider threats by automatically managing complex user control levels, like multiple administrator settings.
Intrusion Detection and Prevention
The distributed architecture of big data is a plus for intrusion attempts. An Intrusion Prevention System (IPS) enables security teams to protect big data platforms from vulnerability exploits by examining network traffic. The IPS often sits directly behind the firewall and isolates the intrusion before it does actual damage.
Centralized Key Management
Key management is the process of protecting cryptographic keys from loss or misuse. Centralized key management offers more efficiency as opposed to distributed or application-specific management. Centralized management systems use a single point to secure keys and access audit logs and policies. A reliable key management system is essential for companies handling sensitive information.
A growing number of companies use big data analytics tools to improve business strategies. That gives cybercriminals 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.
Luckily, smart big data analytics tools can lead to new security strategies when given enough information. For example, security intelligence tools can reach conclusions based on the correlation of security information across different systems. This ability to reinvent security is crucial to the health of networks in a time of continually evolving cyberattacks.