Security in Big Data: Common Issues and Best Practices

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BigDataScalability.com – Big data plays a crucial role in any business these days. With advanced analytics tools, organizations can reveal hidden information for better insights into the market. However, the security aspect requires extra attention as failing to ensure the security in big data can cause data breaches.

Organizations face several security challenges when incorporating big data. Fortunately, there’s always the best solution for any security issue. Let’s have a closer look at privacy and security in big data, their solutions, and best practices to maintain security.

What Is Security in Big Data?

What-Is-Security-in-Big-Data

Big data security can be defined as all the tools and measures employed to protect the data and analytics methods from malicious activities, such as theft, attacks, intrusions, and anything that can cause negative effects to them.

Security and privacy have been a constant concern when it comes to big data as it becomes valuable targets to intruders. Big data saves a variety of information, some of which are confidential and sensitive. This may include but is not limited to banking details, credit card details, and password.

The main question is, why is big data security important? This is because even a single attack on the dataset can cause unprecedented effects like ransom demands, data breaches, and financial losses. Moreover, your IP might be spread to unknown buyers.

Ensuring big data security is essential. Some organizations keep out of intrusions with user authentication, firewalls, intrusion detection system, intrusion protection system, and end-user training. Data encryption can also be a choice to protect the data.

Security in big data often comes with basic architecture that contains several stages, including:

  • Data classification: Data is categorized into normal and sensitive categories by providing a classification algorithm and considering different types of attacks.
  • Data encryption: In this phase, sensitive data is encrypted, commonly with a homomorphic cryptosystem.
  • Data storage: Both normal and sensitive data are stored on different system nodes. ORAM technique is often used in this stage.
  • Data access: End-user who needs to find specific data can use the path hiding approach to acquire the data while keeping it private.

Top Security Issues in Big Data

The significant increase in data sets comes with challenges aside from opportunities. While it promises better decision-making for organizations, it has a few advantages such as security issues, data breaches, and more. Here are several top issues related to security in big data:

1. Data Storage

Data-Storage

Cloud storage offers a convenient solution for organizations and businesses to store their data sets, allowing users to access the data easily. However, this convenience has a shortcoming as it increases the risk of security issues.

Cloud storage doesn’t accept mistakes—even the smallest mistake in controlling the access can allow the third party to host the sensitive data. As a solution, organizations and companies can combine cloud storage and on-premise storage to achieve the ultimate security.

Normal data can be stored in the cloud for easier access while sensitive data can be kept in on-premise storage. However, cybersecurity experts are required to implement on-premise databases and it will increase the cost of data management.

2. Data Privacy

Data-Privacy

Security in big data is often associated with data privacy. As one of the biggest challenges in the digital world, privacy helps protect sensitive and personal information from breaches, attacks, and data loss. It also affects customers’ trust.

Following strict data privacy principles is essential so that businesses can strengthen data protection. Together with access management services and strict privacy compliance, organizations can make sure the data is well-protected.

Some other rules to follow include having more controls over your data backups, understanding your data, protecting the network, performing risk assessments, and training end-users about security and big data privacy.

3. Data Management

Data-Management

Poor data management can lead to a compromised database and security breaches. As it can cause crushing consequences on organizations, initiating a highly secured database is essential to ensure data security at any level.

Businesses can follow physical security practices and software-based security measures to achieve the goal. Some data management methods are worth trying, such as incorporating data encryption and data segmenting, using a trusted server, and securing on-the-move.

Furthermore, some security tools can be integrated to monitor data activity and notify organizations when data sets have been compromised.

4. Fake Data

Fake-Data

Another challenge of security in big data is false information or even fake data. It poses a serious threat to companies or businesses as it wastes time and concentration that could be spent on other issues. Besides, fake data can lower production and critical processes to run businesses.

There’s more chance to acquire false data on a larger scale. One solution to get rid of this issue is to be critical of the acquired data. Organizations and businesses need to make sure they are working on the right data by validating data sources through periodic assessments.

Evaluating machine learning models contribute to obtaining the right data. Conducting diverse tests on datasets can help find anomalies, thus organizations can re-check data validity earlier.

5. Data Poisoning

Data-Poisoning

Chatbots and other machine learning solutions present convenience as interaction with users can improve them. However, data poisoning can be a big challenge for the system. Data poisoning refers to a technique to aggress the training data of machine learning models.

This challenge of security in big data potentially affects the ability of the models to provide accurate predictions. This will result in chaos, such as data injection, data manipulation, and logic corruption.

Outlier detection is considered the best solution to counteract the attack. It helps separate the injected elements in the pool to be separated from the data distribution.

6. Employee Theft

Employee-Theft

In these modern days, employees are allowed to handle critical business information. Apart from they become a big asset for businesses, there’s always a risk of employees leaking sensitive data, either intentionally or not.

One way to prevent employee theft is by implementing legal policies while securing the network using a virtual private network. Desktop as a Service can also be utilized to prevent the employees store the data in their local drives.

Best Big Data Security Tools

Data breaches are still the major issue when it comes to security in big data, following hackers that become more vicious. This is why businesses and organizations need the best data security tools at their disposal. Let’s have a look at the potential tools.

1. FortiGate Next-Generation Firewall

FortiGate-Next-Generation-Firewall

FortiGate Next-Generation Firewall is built for a hybrid environment. This network security platform protects businesses from threats, both external and internal in multi-cloud and hyper-scale data centers. With the ability to update antivirus and malware software constantly, it ensures updated protection.

This security tool comes loaded with advanced features to detect threats, such as SSL inspection, deep content inspection, advanced visibility, intrusion prevention system, and many more.

2. Egnyte

Egnyte

Security in big data is at risk when users access the data and content through multiple locations, such as cloud storage providers, office computers, network drives, and more. To prevent unauthorized data access, Egnyte comes to offer help.

This is a content management platform that is centralized to provide data administration and security for distributed workforces. It has tons of useful features, including content classification patterns, automatic risk scoring, unusual behavior detection, and others.

3. HashiCorp Vault

HashiCorp-Vault

HashiCorp Vault comes to optimize big data security management in businesses and organizations. This platform highlights encryption service that promotes better protection of sensitive data in the cloud. Thanks to the APIs, data encryption and decryption are now simplified.

Not only does it provide excellent protection, but also it offers flexibility as it supports certificates, tokens, encryption keys, or passwords. For developers, an open-source version is available for further customizations.

4. Incydr

Incydr

Incydr is a security tool designed to detect data risks in remote workforces and collaboration-focused businesses. Built for SaaS platform, it protects the data from internal threats which many organizations spend less time on it.

Incydr detects and determines insider threats by evaluating the way the data is used in the organizations. Besides, this platform helps make sure that your network is not prey for phishing attacks or the like.

Best Practices to Maintain Security in Big Data

Best-Practices-to-Maintain-Security-in-Big-Data

Now that you know the challenges of security in big data and the best tools that offer ultimate protection, let’s take a look at the data security best practices. Learning these best practices help organizations reduce the risks related to large data sets management.

1. Improve Protection in Authentication Gateways

Weak authentication is one of the potential factors that lead to data breaches. The vulnerability in authentication allows a hacker to gain access to the data, causing bad effects such as financial losses, damaged reputation, and even legal consequences.

Poor authentication gateways can be prevented by ensuring that no broken tokens are present. This way, unauthorized users or hackers won’t intrude into the data easily.

2. Implement PoPL

The principle of least privilege (PoPL) limits user access to the minimum level. Implementing PoPL and maintaining a tiered access control provides extra protection as only specific users can gain access to the data, not to mention it promotes normal functioning.

Another benefit of employing this principle in maintaining security in big data is that the organizations can prevent IT specialists from conducting unseemly behavior, such as data mining activities.

3. Use Retrospective Attack Simulation

Smaller organizations that rely on cloud-based storage but have financial constraints to create in-house infrastructure can benefit from retrospective attack simulation to find vulnerabilities in their system. The simulation involves third-party applications on the cloud.

When the counterfeit attack succeeds, further investigations must be conducted to find the best solution. This simulation works effectively to prevent genuine hackers from intruding and gaining access to the data due to system weaknesses.

4. Employ Antivirus Protection

A wide selection of antivirus providers offers solutions for improved security in big data. Using the latest antivirus is effective to give updated protection in a big data environment so you can reduce the risk of data breaches, data loss, and others.

For optimum protection, be sure to keep the antivirus updated. Install the latest updates and patches as soon as they are available.

5. Conduct Periodic Audits

The ever-evolving technologies require organizations and businesses to deal with the increasing demand. Scheduling periodic audits is one of the possible ways to find out vulnerabilities caused by improvements in technology.

Once you find new vulnerabilities, you can adjust the security compliance with the current standard so that the data security systems in your businesses can keep up with the new technology.

Data volumes keep increasing day by day, pushing organizations and businesses to put their best effort to protect the data from thefts and attacks. Moreover, some challenges need to be addressed to achieve the goal, from fake data to employee theft.

Luckily, some big data security tools are available to optimize the security in big data. Organizations can also implement data security best practices to prevent unwanted intrusions, hackers, and data loss.

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