LATEST NEWS

DataBank Establishes $725M Financing Facility to Support Growth. Read the press release.

Security Considerations For Big Data In Data Centers

Security Considerations For Big Data In Data Centers


In the modern world, data can be a hugely valuable asset. Its theft or compromise can have major repercussions. Data centers hold vast quantities of data. What’s more, the amount of data they hold continues to grow as the use of big data continues to grow. With that in mind, here is a simple guide to the key security considerations for big data in data centers.

Data privacy and compliance

Many data centers will need to comply with one or more data privacy programs (HIPAA, CCPA, GDPR, etc.). While these will have their own specific requirements, all of them are based on the same basic principles. 

This means that addressing the fundamentals of security considerations for big data in data centers will see you most, if not all, of the way to complying with them. Here are the four key points all data centers need to address.

Encryption for big data

Encryption is arguably the foundation of all data security. If encryption is used effectively, data theft is pointless because the data cannot be used after it is stolen. In modern data security, it is considered essential to encrypt data both at rest and in transit.

In principle, encryption for big data is the same as encryption for any other form of data. In practice, the fact that big data is, literally, a high volume of data, means that it brings a specific set of challenges. 

Probably the most obvious of these is the technical challenge of encrypting and decrypting large volumes of data at the sort of speeds modern users expect (or even demand). Managing the encryption keys is also likely to be more of a challenge than it is with regular data.

Best practices for big data encryption

To address these challenges, organizations should follow best practices:

Using Strong Encryption Algorithms: Employ robust encryption algorithms like AES (Advanced Encryption Standard) for better security.

Implementing key rotation: Regularly change encryption keys to minimize the risk associated with compromised keys.

Secure storage of encryption keys: Ensure encryption keys are securely stored and managed, reducing the chances of unauthorized access.

Access controls and authentication

Implementing robust access controls effectively can be a challenge when dealing with big data. Fortunately, the process can be broken down into five key steps.

Data segmentation: With data segmenting, data is classified according to type and/or level of sensitivity. Access can be granted to any or even all of the data segments according to organization requirements. The fact that any user can access any of the data (if they need it) is what differentiates data segmentation from data silos.

Role-based access controls: Using RBACs means that users only have access to the data that is directly relevant to their function.

Permission management: This is a subset of RBACs but it’s worth highlighting on its own. Permission management defines the extent (if any) to which users can manipulate the data to which they have access.

Multi-Factor Authentication (MFA): MFA enhances security by combining something the user knows (e.g., a password) with something they have (e.g., a fingerprint or a mobile app-generated code). The use of MFA is now standard across sensitive applications (even in the consumer segment). It should certainly be used for big data in data centers.

Logging and auditing: Logging and auditing are essential for verifying that what is actually happening is in line with what you think ought to be happening.

Threat detection, prevention, and response

Given that data is so valuable, it follows that big data needs strong protection against threats both internal and external. This means that real-time monitoring systems are essential for threat detection. It can often be very beneficial to use AI-powered solutions. 

AI can also be very helpful in threat prevention. In particular, it can be used in behavioral analytics. Behavioral analytics assesses user behavior and identifies anomalies or suspicious actions. It creates user profiles to understand typical behavior, making it easier to spot deviations.

With that said, you still need to work on the assumption that your security defenses are going to be breached from time to time. This means you need an effective incident response plan in place. 

Your incident response plan should cover the entire lifecycle of the incident from identification and classification to closure and lessons learned. It should be periodically tested to ensure that it works as expected.

Physical security measures

With data centers, physical security starts with the choice of location. A location needs to have access to critical infrastructure and have minimal vulnerability to environmental threats. At the same time, it needs to be possible to implement effective physical access controls. 

This means that a location needs to have a secure perimeter and monitored entry/exit points. Ideally, the location should be camouflaged in some way (e.g. with landscaping). At a minimum, its address should only be disclosed on a need-to-know basis.

 

Related Resources:

Big Data Storage Use In Data Centers Globally 2015-2024
Optimizing Data Center Infrastructure for Big Data Workloads

Share Article



Categories

Discover the DataBank Difference

Discover the DataBank Difference

Explore the eight critical factors that define our Data Center Evolved approach and set us apart from other providers.
Download Now
Get Started

Get Started

Discover the DataBank Difference today:
Hybrid infrastructure solutions with boundless edge reach and a human touch.

Get A Quote

Request a Quote

Tell us about your infrastructure requirements and how to reach you, and one of the team members will be in touch.

Schedule a Tour

Tour Our Facilities

Let us know which data center you’d like to visit and how to reach you, and one of the team members will be in touch shortly.