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5 Steps to Ensure Secure Data Flow in Your Organization

Image by <a href="https://pixabay.com/users/4832970-4832970/?utm_source=link-attribution&utm_medium=referral&utm_campaign=image&utm_content=3044387">Yan Wong</a> from <a href="https://pixabay.com/?utm_source=link-attribution&utm_medium=referral&utm_campaign=image&utm_content=3044387">Pixabay</a>

Image by Yan Wong from Pixabay

Every business has its fair share of sensitive data, both inbound and outbound. Most companies install anti-malware solutions at several access points to guard their data against attacks. Today, companies cannot choose between data innovation and security. That’s why they must make a conscious effort to ensure the secure flow of data within the organization for uninterrupted data innovation. Here are five steps to ensure that will help your organization. 

1. Consider Your Data as a Whole

Instead of treating each entry point as a distinct anti-malware project, businesses should approach safe data workflow as a comprehensive process that tackles data cyber-security across the board. Organizations can gain a comprehensive view of their data reserves by integrating several components into a larger whole, thereby reducing the possibility of neglecting certain data entry points and allowing them to profit from synergies and enhance efficiency. 

2. Identify Sensitive Information

Companies that want to secure their sensitive data must first identify it and its flow. This data can be in the form of names, client information, or employees’ private information. It should be identified by security systems, especially in non-production environments. This process provides organizations with an enterprise-wide perspective of potential challenges surrounding data leaks, allowing them to implement the appropriate measures. Organizations must also establish where sensitive data is coming from and where it is going.

3. Mask Sensitive Data

Data masking is a technique for creating an inaccurate but realistic replica of your organization’s critical data. The objective is to safeguard sensitive data while offering a functioning substitute when actual data is not required. Data masking is the industry standard for protecting non-production data since it mitigates both inner and external risks while retaining the data’s value to consumers. Once the information is masked, it may be transferred and exchanged as needed without the potential risk of data loss. 

4. Distribute Data On a Need-to-Know Basis 

Only share critical information with personnel whose jobs would be impossible to complete without the data considered. This is especially vital in the case of organizations that use a request-fulfill system for internal data distribution. Using a platform-based approach to data distribution can enable organizations to simplify their delivery procedures while maintaining safe data flow. During this process, research what is a data governance maturity model and identify whether or not your organization works on a data governance maturity model. 

5. Conduct Thorough Risk Analysis

Identify the possible risks that may arise during the data flow process so you can preemptively take the steps necessary to mitigate them using data governance tools like Ovaledge. Assess vulnerabilities in your current data flow system and make a conscious effort to overcome them. While storing critical information, organize and store data based on the risk involved, with more sensitive information stored more securely and requiring specific authentication information to access. 

Companies need to be actively involved in maintaining the secure flow of data for business intelligence. Making a conscious effort to ensure secure data flow can significantly mitigate the risks of data loss, piracy, and data leaks.



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