Big Data Privacy and Security Challenges: What you need to know

| Dimitri Antonenko Last updated 31 Jul, 2022

Big Data Privacy and Security: In today’s data-driven world, big data is vital for any organization to prosper. Several innovative infrastructures have enabled enterprises to expedite the data flow for real-time delivery of insights and improved decision making. Many open source big data tools are not developed with security in mind. Big data poses several security threats that could be detrimental to enterprises.

Failure to implement security measures when storing and processing massive amounts of data might result in data breaches. Simplifying the accessibility of data is vital for businesses, but having control over big data is equally crucial for fostering customer trust.

Considering big data security as a low priority and putting it off till the later stages of a big data adoption project isn’t sensible.

Taking a security-first approach and acknowledging big data security comes with concerns and challenges, which is why getting acquainted with them is more than helpful.

Below, we discuss security concerns relating to big data privacy and explore the security challenges and threats big data privacy can face.

On this page:

What is Big Data Privacy and Security?
Big Data Security and Privacy challenges
Addressing Big Data Security Threats
Next Steps: Big data privacy tools – What to look for

What is Big Data Privacy and Security?

Big data refers to large, diversified sets of data sourcing from multiple channels; social media platforms, websites, electronic check-ins, sensors, product purchases, and call logs. The choices are limitless. Big data has three unique characteristics: volume, velocity, and variety.

Processing Big data has become the technique of choice for collecting information that might enhance business operations further. However, massive data volumes are constantly susceptible to dangers and security challenges.

Big data privacy entails effectively handling big data to minimize risk and secure sensitive data. Because big data consists of enormous and complex data sets, many standard privacy mechanisms cannot keep up with the requisite scale and velocity.

To protect big data and guarantee that it can be used for analytics, you must develop a framework for privacy protection that can accommodate the volume, velocity, diversity, and volume of big data as it is transferred between environments, processed, analyzed, and shared.

The consequences of the thefts and risks a big data framework faces are enormous. It can be even riskier when the organizations store sensitive or confidential information like credit card numbers or customer information.

Attacks on big data systems like information theft, DDoS attacks, ransomware, or other malicious activities can originate offline or online and crash a system. These security challenges also affect the cloud, so taking all the precautionary steps and creating a protected security system for big data is necessary.

Big Data Security and Privacy challenges

Here are some of the security challenges that big data faces:

1. Data storage

Businesses are utilizing cloud data storage to accelerate their data transfer and operations. However, security concerns exponentially increase the associated hazards. Even the most minor oversight in managing data access can allow anyone to obtain an abundance of critical information.

As a result, big tech companies embrace on-premise and cloud data storage to obtain Security and flexibility. Mission-critical information can be stored in the on-premise database while the less sensitive data is kept in the cloud for ease of use. But implanting security policies in an on-premise database requires cybersecurity, which increases the costs of managing data.

Still, it’s better to spend a little more than risk big data’s security.

2. Endpoint vulnerabilities

Cybercriminals can easily modify data on endpoint devices and transmit fraudulent data to data lakes. Security solutions that analyze logs from endpoints must verify the endpoints’ authenticity.

For instance, hackers can enter industrial systems that utilize sensors to detect process failures. After getting access, hackers alter sensor readings to be false. Typically, such problems are resolved with fraud detection technologies.

3. High speed of NoSQL databases’ evolution and lack of security focus

This point may seem advantageous but, in reality, is a serious concern. Presently, NoSQL databases are a popular trend in big data science. It is precisely that popularity that causes the problems.

Technically, NoSQL databases are continuously honed with new features. And once again, security is being neglected and forgotten in the background. It is hoped that the big data security solutions will be provided externally. But is somewhat ignored even on that level.

4. Struggles with granular access control

Sometimes, database items fall under restrictions, and practically no users can see the secret information, like personal info in medical records, etc. But some parts of such items could theoretically be helpful for users with no access to them, say, for medical researchers.

Nevertheless, all the valuable content is hidden from them. Using granular access, people can access needed data sets but can only view the info they are allowed to see. With big data, granting and controlling such access is difficult since big data technologies weren’t initially designed to do so.

The solution is that the needed data sets – that users have the authorization to view – are copied to a separate big data warehouse.

5. Employee theft

Advanced data culture has allowed every employee to hold a certain level of critical business information. While it boosts data democratization, the risk of an employee leaking sensitive information intentionally or unintentionally is high.

Employee theft is possible not only in big companies but also in small startup businesses. To avoid employee theft, companies have to implement legal policies along with securing the network with a virtual private network.

In addition, companies can also use a Desktop as a service (DaaS) to eliminate the need for data stored in local drives.

Addressing Big Data Security Threats

Tools for protecting massive data are not new. They are more scalable and capable of securing numerous data kinds. The list below describes common data security strategies.

Encryption

Encryption technologies for big data must protect data at rest and in transit across massive data volumes. Additionally, businesses must encrypt both user- and machine-generated data.

Encryption technologies must therefore support many big data storage formats, such as NoSQL databases and distributed file systems like Hadoop.

User Access Control

User authorization is a fundamental network security mechanism. The absence of adequate access control measures can be catastrophic for large-scale data systems. The foundation of a comprehensive user control policy must be automated role-based settings and policies.

Policy-driven access control safeguards big data platforms from insider threats by automatically managing complicated user control levels, such as numerous administrator settings.

Intrusion Detection and Prevention

Big data’s dispersed architecture is advantageous for infiltration attempts. An Intrusion Prevention System (IPS) analyzes the network traffic and helps security teams defend large data platforms against vulnerability attacks.

The IPS frequently resides behind the firewall and isolates intrusions before they may cause significant damage.

Centralized Key Administration

Key management is safeguarding cryptographic keys against misuse and loss. Centralized key management is more efficient than distributed or application-specific administration.

Keys, audit logs, and rules can be accessed and secured through a central point in centralized management systems. A dependable key management system is required for businesses that handle sensitive information.

Next Steps: Big data privacy tools – What to look for

For organizations handling sensitive and large volumes of data, proper data management is vital – No privacy regulation or law will compensate for poor data management.

As major companies, organizations even startups are using big data analytics tools to improve business strategies, data management, cost reduction of hardware, and several other corners, it comes with various security challenges too.

The below features should be considered when reviewing data privacy tools:

Cloud-based: A big data privacy tool must be compatible with cloud computing. If it solely operates on a physical server or computer, it is likely an obsolete solution that cannot keep up with the privacy challenges and regulations associated with big data in the present day.
User-focused: Adoption is essential, and data security is a collaborative effort. The appropriate tool should be user-friendly at all organizational levels. Finding a simple and user-friendly tool increases team confidence and adoption.
Automated: Choose a technology that employs machine learning that helps you to automate and enhance your data quality and privacy protection, allowing you to concentrate on making confident decisions based on reliable data.

Businesses and individual users are not always aware of what occurs with their data or where it is stored. Intelligent big data analytics technologies can lead to new security solutions with sufficient data.

All organizations and businesses must make security their priority. They should plan and create a whole security system with a team so that their data can be protected along with the customers and business.