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Last Updated: Mar 27, 2024

Security Infrastructure

Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

The more significant big data analysis becomes for businesses, the more critical data security. For example, if you work in healthcare, you'll undoubtedly want to employ big data applications to track changes in demographics or patient demands. This information on your constituents must be kept secure to meet regulatory requirements and safeguard the patients' privacy. You'll need to consider who is allowed to see the data and under what conditions they can do so. You'll need to be able to both authenticate and secure the identities of users and patients. These security concerns should be built into the big data fabric from the beginning rather than being an afterthought.

Challenges in Data Security

The requirements of security and privacy of the big data stack are similar to the requirements for other data environments. Specific business needs must be closely matched with data security standards. When big data is used in a plan, it presents certain distinct challenges:

  1. Data access: The technical requirements for user access to big data are similar to those for non-big data solutions. Only those having a valid business need to examine or interact with the data should have access to it. Most fundamental data storage platforms have stringent data security measures in place and federated identity capabilities that allow for adequate access across the architecture's various layers.
  2. Application data access: From a technical standpoint, application data access is also pretty simple. Most application programming interfaces (APIs) provide security against unauthorized use or access. For most large data applications, this level of protection is likely sufficient.
  3. Data encryption is the most challenging part of data security in a large data environment. Encrypting and decrypting data in typical contexts puts a lot of strain on the systems' resources. With extensive data, this challenge is amplified. The most straightforward solution is to increase computational capability. A more moderate method would be to determine the data elements that require this level of security and encrypt only those that are needed.
  4. Threat detection: The addition of mobile devices and social media sites rapidly increases the amount of data and the potential for security risks. As a result, businesses must take a multiparameter strategy for data security.
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Security Infrastructure

The physical infrastructure supports everything, while security infrastructure safeguards everything in your big data ecosystem. The interfaces are the next level in the stack, providing bidirectional access to all of the stack's components, from business programs to data streams from the Internet.

Establishing a standard framework that can be shared both inside and outside the firm and with technology and business partners is a crucial aspect of the design of these interfaces.

Application Programming Interfaces

For decades, programmers have used APIs to enable access to and from software implementations. Tools and technology providers will go to considerable lengths to develop new applications with their products as simply as possible. Although highly beneficial, IT experts may be required to design unique or proprietary APIs only available to the firm.

It is not a straightforward task, and you may need to accomplish it for competitive advantage, a particular need within your firm, or some other business necessity. APIs must be thoroughly documented and maintained to keep their commercial value. As a result, several businesses opt to use API toolkits to gain a head start on this crucial task.

5 Steps of data security

  1. Authentication: Are the users that have access to data files who they claim to be? We've all heard of the primary password and the more complex two-factor authentication. Big data authentication systems might create a user profile as a checklist when granting or refusing access.
  2. Authorization: Big data systems must be able to assess what type of data the user should have access to and what known users can (and cannot) do with that data after authentication. Are users gaining access to information that they don't have the authorization to see? Are they misinterpreting the data?
  3. Data Protection: How will your company secure data and prevent it from being exposed to unauthorized users? Information about consumers or employees must always be kept out of the public eye.
  4. Auditing: Is your firm keeping track of who and when data was accessed? This data could be crucial in determining whether an external breach has happened or reviewing a data security policy that deviates from standards and regulations.
  5. Row-level data security: This limits the data sets to which users have access. Users need not have access to the entire database. It's critical to restrict user access to specific rows within a data collection.

Frequently Asked Questions

  1. Why is data security important?
    Ans: Companies collect their user’s data for data analysis. The company must ensure that this data is secure to prevent its misuse. This makes data security very important.
  2. What is user authentication?
    Ans: A company must ensure whether the application user is who he claims to be. Mostly password authentication or two-factor authentication is used by companies.
  3. What is user authorization?
    Ans: Big data systems must assess what type of data the user should have access to and what known users can (and cannot) do with that data after authentication. Authorization restricts the information that a user can access.

Conclusion

In this article, we studied in detail the security infrastructure in a big data stack. We saw the challenges in data security and essential points of a security infrastructure for big data. We hope that this blog has helped you enhance your knowledge of security infrastructure and if you would like to learn more, check out our articles on code studio. Do upvote our blog to help other ninjas grow. Happy Coding!

Topics covered
1.
Introduction
2.
Challenges in Data Security
3.
Security Infrastructure
4.
Application Programming Interfaces
5.
5 Steps of data security
6.
Frequently Asked Questions
7.
Conclusion