4 Capabilities of Data Fabric Architecture That Help Companies Protect Information

Businesses gather, hold, and generate enormous amounts of data. Depending on the industry and services they provide, they have personally identifiable user information and data concerning the operations within a corporation.

Then, some data is continually being generated from versatile software integrated into the premises, such as that coming from security solutions.

As a result, data is flooding systems around the clock. How can businesses keep up and, at the same time, ensure that sensitive information is kept secure?

How can you turn the disarray of information into a streamlined data management system?

This is where data fabric, an architecture that enables teams to govern data from one endpoint to another, comes into the picture. Below we explore the four capabilities of data fabric architecture that help companies protect information.

Indexing and Keeping an Eye on Sensitive Information

Keeping up with large amounts of data that are collected and stored has become a major challenge for companies. Regardless, continually managing databases is a must.

What Should Data Management Entail?

Within the data fabric infrastructure, all information is:

  • Identified — based on which types of data are in the network and whether they’re of sensitive nature
  • Cleaned — corrupted or incomplete data is removed since it’s not useable
  • Catalogued — according to types, to give teams an overview of what kind of data the company has at its disposal
  • Secured — sensitive data is kept far away from potential intruders

As with any other data, sensitive information has to go through the process above. Once it does, it gives the security teams an insight into where the private data is and who has access to it.

The discovery of sensitive information within the system and having a bird’s eye view of where it is at all times is just the tip of the iceberg.

Thousands of files that go through the management system are not necessarily clean. These can be corrupted, incorrectly formatted, or incomplete.

Even more, big data types come in all shapes and sizes, varying from semi-structured to structured as well as completely unstructured data.

Tracking Cyber Threats That Can Expose Data

The high number of data breaches that companies have experienced over the last few years has proven time and time again that hackers are predominantly after data.

Why is This The Case?

First, most cybercrime is financially motivated (as opposed to ruining a competitor’s reputation). Hackers target data that is stored in the systems to sell them on the dark web or to demand ransom from victimised companies.

They know how damaging it is for the company if the personal data of their users get leaked, stolen, or sold on hacking forums.

Second, the data that security analysts are interested in is not only sensitive but is generated from siloed security tools which show the current state of the security and possible high-risk vulnerabilities.

What might start with a typical hacking attempt — exploitation of vulnerabilities to gain access into the system — can create a pathway leading to sensitive data.

It’s all connected.

Proper data management lets teams know if a hacking activity is endangering private user data or not.

Managing Data Access Policies

Users should indeed have easy access to data that is clean and catalogued (effortless to use and make sense of). However, an organisation also needs to know who can access the data at any time and what the information is used for within the premises.

This is integral information that can help uncover whether a hacker or a genuine user has accessed the system.


When data goes through the process of detection and identification, it should also be categorised based on who can access it. Therefore, it’s important to set data access policies that make sense for a company.

Some of the factors based on which data access can be pre-set include:

  • The type of data that is catalogued within the data fabric architecture
  • The role of the team member within the company (e.g. introducing role-based access control for the cloud)

In the case that hackers get the username and password of a single user, they shouldn’t be given access to the complete infrastructure, not even if the user has high levels of access.

Limit the access privileges based on which information each person needs to do their job. This helps security teams get a better grasp on who can access sensitive data.

Automating Data Fabric Processes

In a nutshell, catalogue, analyse, and track the data within the company’s premises, especially sensitive corporate and user data. Security fabric specialises in data that is constantly scaling, growing, and being altered and used within the system.

The architecture ensures that the data entered into the network doesn’t get a life of its own.

Most importantly, it also secures sensitive data by letting the team know where it is and who has access to it at all times. This information is then linked to the potentially harmful threat actor activity that is detected in the system and thus can be used to catch threat actors in their tracks.

Data security fabric repeats this process continuously.

All of the data management is automated to ensure that the security teams have complete visibility around the clock and can react to malicious hackers after sensitive information early, preventing cybercrime in its tracks.