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Home BIG DATA How Can Organizations Protect Sensitive Data In Big Data Environments?

How Can Organizations Protect Sensitive Data In Big Data Environments?

Managing a big data environment is challenging, especially when you have sensitive, high-risk information to protect. Your organization can take steps to make your cybersecurity more agile and robust so you can safeguard vulnerable data without over-complicating your network. Here are the top strategies you can use to protect it.

Implement Network Segmentation

One of the first things organizations should do to protect their sensitive data is implement network segmentation. This is a flexible and highly effective strategy for keeping vulnerable information safe, even in big data environments. It can also minimize the threat of cyber incidents in worst-case scenarios.

Network segmentation involves breaking up your organization’s network into multiple chunks with layers of security isolating them from each other. They all live under the roof of one system, but movement between the segments is restricted. You can customize the safeguards for different layers, allowing you to have more open and heavily protected data groups in the same environment.

There are a few ways you can implement network segmentation. Most strategies employ a combination of physical and virtual tools. For example, firewalls are a core technology for segmenting networks. You can also use software like VLANs and network overlays, as well as identity management products like access control lists.

Segment your network based on levels of risk. Low-risk data is usually more easily accessible since users often need it regularly. In contrast, high-risk data should have finely tuned firewalls and access controls separating it from the rest of the environment. This will make it harder to access, but that shouldn’t impact the user experience since it shouldn’t be accessed frequently.

Increase Network Visibility

Visibility is crucial for protecting sensitive information. One of the most common drawbacks of a big data environment is the sheer volume to monitor. It’s easy for unusual activity or exposed data to go unnoticed. Hackers often exploit that weakness.

Increasing visibility is vital to eliminate this risk factor. In fact, poor visibility is one of the top indicators your organization might need to upgrade its computing infrastructure. Disorganized networks with poor communication often suffer from low data clarity, leaving them highly exposed.

There are several ways you can improve network organization and transparency. Automated monitoring is a great option, particularly for big data environments. Visibility will naturally be a challenge if you have a lot of information to track. Automating some monitoring tasks can reduce the workload and make effective monitoring more achievable.

Additionally, automated monitoring will significantly improve your threat detection capabilities. It’s all too easy for breaches to go unnoticed in big data environments. Time is critical for minimizing the threat of a hack, though. Automation enables you to detect suspicious activity sooner rather than later.

As a general rule, automating security features as much as safely possible will simplify things in a big data environment. You can automate repetitive tasks like flagging suspicious activity, rejecting unauthorized access, encrypting sensitive information and more. Utilizing automated security tools will make protecting vulnerable data much easier for your security team.

Another vital aspect of improving network visibility is understanding your entire computing environment. Take time to completely map out your data, users, traffic patterns and security protocols. It’s much easier to see where you’re going when you have a road map. Plus, the network mapping process often highlights existing vulnerabilities and weaknesses.

Also Read: Cybersecurity: How To Properly Protect Your Professional Email?

Make Access Control a Top Priority

Identity and access management should be part of every organization’s cybersecurity strategy, but it’s especially important for big data environments. Access control can help with network organization and visibility. It’s one of the foundational methods for keeping sensitive information safe, even in a large, dispersed system.

The principle of least privilege is a great place to start. This approach to access control only grants users the absolute minimum amount of access they need and nothing more. It often goes hand in hand with zero-trust security, which uses continuous verification to confirm user authorization.

Both of these can also factor into your network segmentation strategy. You can restrict access to entire segments and use more granular control for specific files or applications that are especially vulnerable.

With this type of data, it is usually best to create a short white list of approved users rather than a much longer black list of unauthorized ones. As with the least privilege approach, limit your safelist to only the people who absolutely need to access the sensitive data and no one else.

Physical security is also important to address. Big data environments can be fully cloud-based, all on-prem or a hybrid combination of infrastructure models. Regardless, the information is still ultimately tied to a physical server somewhere in the world. When selecting a data center provider or managing in-house servers, it’s crucial to ensure physical access control protocols are in place.

On-site server security can be automated or contracted out, much like virtual security automation. This is the case with most cloud providers and colocation data centers, which often provide in-house security services like 24/7 surveillance, advanced access control, alarm systems and more.

You may be able to have sensitive data stored on one or two isolated server racks if you want to ensure maximum on-site security for specific information. Work with your in-house IT team or data center partner to determine the best way to physically secure servers.

Conduct Tests and Audits Regularly

Testing and audits are essential components of any robust cybersecurity strategy. They’re a great way to regularly check in on the health and effectiveness of your security protocols and ensure you are adapting to new threats.

You can use these tests to verify that your sensitive data has the best security possible. During penetration testing, you can even prioritize certain information so the tester can direct their focus there.

You’ll know you most likely have strong protections if the tester can’t successfully access your organization’s sensitive data. If they do succeed, they can help you identify and eliminate vulnerabilities so real hackers can’t get through. Either way, testing is invaluable for protecting your information.

You can hire a white hat hacker to put your big data environment to the test. This is someone with hands-on experience in hacking who uses their knowledge to help security teams rather than commit cybercrime.

White hat hackers know how cybercriminals would look at a network. This unique perspective allows them to see weaknesses others wouldn’t notice. They may be able to identify vulnerabilities even a penetration tester might miss.

Additionally, consider adopting a formal cybersecurity framework. NIST is among the most popular today, particularly in the United States. It has a large community that offers best practices, tips and guidance, as well as audit support. Security frameworks can help you stay ahead of emerging threats and leverage expert advice in your strategy.

Ensuring Security in a Big Data Environment

Managing a big data environment can be daunting, especially when it includes pockets of sensitive information requiring more protection. You can utilize several strategies to protect high-risk data, including network segmentation, automated monitoring, least-privilege access control and penetration testing. These tactics will build layers of security and increase visibility.

Also Read: Apps And Data Protection – How To Secure Your Data

Eleanor Hecks
Eleanor Hecks is the managing editor at Designerly. She’s also a user interface designer with a focus on mobile apps. Connect with her about graphic design, UX or tea on LinkedIn.

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