What is Big Data Security? Top Challenges and Solutions
Big data deployments are very valuable for a business. Big data security is a term that represents all the tools used for protecting the big data and analytics process from theft, attacks, and more.Thank you for reading this post, don't forget to subscribe!
If there’s an attack, an unauthorized user can spread IP everywhere. As a result, you have to face reputational losses and fines. Moreover, an attack on your company’s big data storage could cause a huge financial loss.
By reading this article, you will find out about big data security, how to implement it, and its challenges. Let’s get started.
What is Big Data Security?
The goal of Big Data Security is clear; keeping out unauthorized intrusions with strong user authentication, firewalls, IDS, and IPS. Even it sounds like other network security strategies, big data allows an extra layer of protection. There are many other advantages of leveraging Big data for small businesses as well.
Its essential cause security tools need to operate three stages that are not present in the network. Let’s find out about these three stages:
Stage One: Data Sources
A company’s big data comes from various data sources. However, there are three primary sources of big data, social data, machine data, and transactional data. Social data comes from various social media activities such as likes, comments, tweets, video uploads, and more. Machine data comes from installing industrial equipment while transactional data comes from payment orders, delivery receipts, and invoices.
Stage Two: Stored Data
You need strong security toolsets such as planning, strong user authentication, and intrusion protection to protect stored data. Additionally, you need to run big data security toolsets with a distributed cluster platform. Plus, your company’s tools need to protect log files as well.
Stage Three: Output Data
The main goal of using a big data platform is running meaningful analytics across different types of data. This data analytics output gives the result to the dashboard, reports, and applications.
How Can You Implement Big Data Security?
There are several ways you can implement big data security to protect your company’s analytics tools. However, most organizations use Encryption for this process. Encryption is a simple yet effective tool for big data protection.
Encrypted data is not useful for hackers as they are not getting the key to access it. The best thing about the encryption tool is, that both input and output information is protected.
Alternatively, you can build a strong firewall to protect the security tools. A firewall can help by filtering traffic that enters and leaves your company’s servers. You can prevent attacks by checking a third party’s access.
[Read more: What is big data and why it is important?]
Big Data Security Challenges
You will face several challenges while securing big data. However, you have to understand that these challenges can pertain to the cloud as well. So, don’t take anything for granted when you are hosting a big data platform in the cloud.
- Advanced analytics tools and nonrelational databases are new technologies compared to other tools. It can be challenging for security software.
- Sometimes big data administrators mine data without giving notification. So, your security tool needs to alert you of suspicious access.
- Big data security tools can protect data storage and ingress. However, they are not that effective when it comes to data output from multiple tools.
- The size of data installation can be too big for routine security audits. So, this can introduce multiple vulnerabilities on several nodes and servers.
Big Data Security Issues
1. Fake Data Generation
Fake data generation is a serious threat to businesses because it consumes time that could otherwise be spent identifying and resolving other pressing issues. False flags for fake data can also lead to unnecessary actions that can potentially reduce production or other critical business processes. One way to avoid this is for businesses to be critical of the data they are working on to improve business processes. An ideal approach would be to validate the data sources on a regular basis and evaluate Machine Learning models with diverse test datasets to detect anomalies.
2. Employee Theft
Every employee now has access to a certain amount of crucial business information thanks to advancements in data culture. Although it promotes data democratization, there is a great likelihood that a worker may mistakenly or intentionally disclose critical information. Startups as well as established tech organizations frequently experience employee theft. Companies must create legal procedures and secure the network with a virtual private network to prevent employee theft. Additionally, businesses can do away with the capabilities of data kept on local discs by using a Desktop as a Service (DaaS).
3. Data Poisoning
There are several machine learning solutions available today, such as chatbots, that are educated on enormous amounts of data. Such systems have the benefit of continuously improving as people interact with them. However, this results in Data Poisoning, a method for attacking the training data for machine learning models. As the altered training data may impact the model’s capacity to make accurate predictions, it might be viewed as an integrity attack. Catastrophic outcomes include logic corruption, data manipulation, and data injection. The best method of combating the evasion is outlier detection, which allows the training pool’s injected pieces to be distinguished from the data distribution as it now exists.
Big Data Security Technologies
You need to use various tools to secure big data. Well, these tools are not new. They only can secure different types of data.
1. User Access Control
User access control is one of the most basic security tools, but many people prefer it for their companies. If you want to use a strong user access control, it requires a policy-based approach. So, it can automate role-based settings.
Most companies use the Encryption tool for securing data-in-transit and at-rest. Well, these tools need to do this for a large volume of data. Moreover, Encryption tools need to manage different types of data such as machine-generated and user data. These tools also work on different toolsets.
3. Centralized Key Management
Centralized Key Management is one of the best practices for big data security. Just like other technologies, this one applies strongly in big data environments. The best thing about it is people are using this tool for years.
4. Physical Security
While ensuring virtual security, many companies forget about physical security. Make sure you are building it into your own data center. Most physical security systems deny the access of strangers. You can use security logs and video surveillance for physical security.
Finally, you get to know what is big data security. It plays a crucial role to protect your organization’s big data analytics tools. There are several ways you can use for protecting your tools. However, make sure you are choosing them as per requirements.
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