Best Data Governance Application Practices For Secure and Clean Data

But how to do it the right way?

Proper data governance should be:

• Secure from hacking threats — sensitive information in particular
• High-quality
• Consistent across versatile platforms
• Available for specific business functions
• Easy to understand
• Neatly organized

In reality, data management practices tend to be:

• Chaotic
• Outdated
• Hard to search
• Not automated to the desired degree
• Difficult to integrate into the company’s architecture

With more information to manage than ever before, applying the previously mentioned best practices is a challenge for companies that want safe and clean data.

What are some of the top data governance applications you need to know when working towards more secure and less chaotic management?

Let’s find out.

Automation of Data Management Processes

With such a high volume of data nowadays, automated management is a necessity. It’s also essential for making company data safe and organized in the scaling process.

Some of the processes that can be automated are the classification and identification of data.

Once the organization knows what kind of data they have and where it is within the system, it can enforce the necessary policies that protect them.

Another problem that automation of the data solves is the proper management of high volumes of data.

New information is coming into the network but is also being generated within the infrastructure and often moved from one part of the infrastructure to another.

This means that it requires continual classification and discovery.

To sum up, automation should be able to handle:

• Scanning to uncover any new data within the system
• Identification of the information within the system
• Cataloging of data

Securing Data From Theft and Leaks

As mentioned, automation has an important role in the security of data. It enforces the continual scanning, discovery, and cataloging of private information.

During data breaches, malicious actors are typically solely interested in sensitive information. They’re after the resource for which they can demand ransom in exchange for not leaking sensitive files or they intend to sell the data on the dark web or hacking forums. All in all, not a great situation to be the victim of.

To secure the data, it’s necessary to keep the bad actors far away from it.

One of the best practices for securing data is to keep an eye on it — using automated tools to figure out where the data is within the system at all times.

This information helps security teams to figure out who has access to the possibly sensitive information.

Also, another important element when it comes to securing the data is restricting who has the access to sensitive information.

For example, one common way to do so is to implement a zero-trust policy within the infrastructure — implementing role-based access (depending on one’s role within the company) is one way to do it.

If the actors obtain the credentials of a single employee, their password and username should grant them access to the deepest parts of your network.

Even if they use the credentials for illicit access, the system shouldn’t automatically conclude that the person using the credentials is in fact a genuine user who has the right to access their account.

Setting the Data in Context

Data put in context — also known as information, is the key to proper data governance and securing it from bad actors.

This is why knowing which data a company has, as well as who can alter and access it matters.

For instance, automation paired with correlation in data analysis can flag potentially malicious activity — such as a person accessing data outside of the working hours of a company.

Therefore, the context is extremely important for security analysts. It helps them figure out whether the malicious intruder has access to the data or is a genuine user.

In case the incident has already taken place, the context can aid businesses to uncover how much and which data was exposed in a breach as well as which user data is in the hacker’s hands.

For instance, insider threats, such as employees misusing their access, can threaten sensitive data. Threat actors could then obtain these credentials on the dark web and use them to log into an employee's account.

If the information that has been stolen and leaked is the personal data of clients who use the services of a company, this means that such users are now potential victims of further phishing attacks and possible identity theft.

Automation paired with the contextual analysis of data can prevent such a domino effect.

The thing is, the context of how the data is used and where it is within the system is changing all the time. This requires continual and automated scanning for the data, its classification, and identification within the context in which it’s used.

In a Nutshell: Automate, Restrict Access, And Provide Context

Businesses collect a lot of data from their users, but also create a lot of new information within their systems.

As a result, they have more data to govern than ever before. Here, we're also talking about data that is scattered in versatile repositories.

Types of data are varied as well.

To complicate things even further, the data is placed within complex modern infrastructures.

Overall, governing data is even more complex and nuanced than you might imagine. This is also the process that goes beyond securing sensitive data.

Protecting the assets that the business has and having a bird's eye view of the information is essential. Know who has the access to the sensitive data within your company.

Automating the processes that have to be done at all times — such as the discovery of data, and their classification — essentially increasing the visibility of the attack surface for security teams is a must.

Know what kind of data you have, where it is, who can access and use it — and repeat.

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