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How to Implement an Enterprise Level Data Protection Framework.

Data protection is becoming increasingly important as businesses manage an increasingly growing influx of data. Data protection is the process by which organizations ensure that their data is safeguarded from corruption, compromise and loss. Many organizations rely on data for their livelihood. It is integral to organizational health to implement a solid data protection framework.

What to consider when building a data protection framework

Know Privacy Regulation

Businesses must ensure that all their interactions with data are compliant. Therefore it is important the data protection framework is based around adhering to privacy standards. Organizations must have a strong understanding of the strict regulatory laws that are relevant to them such as HIPAA, GDPR and the Payment Card Industry Security Standard. Failure to comply with privacy regulation risks heavy fines that cripple businesses.

Know what needs to be protected

Organizations must identify the volume of sensitive data handled and the degree of its sensitivity. They especially need to know if the type of data they use and hold ought to be protected under the relevant regulations.

Building the data protection framework

1. Identify data owners

Data owners oversee how data sets are used within the organization, with knowledge of which critical data sets are kept and where they are stored. They have control over which data citizens are allowed access to information assets and are responsible for overall data quality. It is essential to have these owners in place so that data is used in a controlled and compliant manner. These owners could be likened to managers at a fast-food chain to ensure smooth functioning.

2. Organize the data

Many enterprises face significant difficulty with data protection management. One issue is that their systems lack cohesion. Often there are many different softwares and programs used by different teams in order to achieve different goals. This leads to the data being scattered and disorganized. Another issue is that there can be unstructured data that is difficult to interpret. Unfortunately, this is commonly the majority of a business’ data. It is essential for organizations to have data across different systems being organized clearly and made accessible to the relevant personnel.

3. Assess the enterprise data

The data protection framework should be tailored to protecting the types of data within an organization and the threats that they face. Organizations need to know the volume of data they handle and the different types of data they possess. Any types of data with sensitive elements, such as health records or payment information, need to be classified as a critical data element (CDE). This will allow the information to be easily identified by data citizens to be used appropriately. This can be a lengthy task that is difficult to be done accurately if not done with proper data discovery tools.

4. Incorporate data policy into the system

With CDEs identified to ensure that they are not used without authorization, compliance controls should be implemented within the system. Depending on what data elements the CDEs have, their access should be regulated accordingly. This means that only data citizens that have been approved to access certain CDEs are permitted to, and can only act within the confines of the compliance controls associated with the data. Businesses can assign their own classification levels but they generally fall under public, internal, confidential and restricted.

5. Integrate an automated security system

Given the high volume of data that enterprises handle, it is inefficient and often impossible for it to be monitored manually. Vulnerabilities that can lead to mishandling within the organization or attack by malicious outside sources can be easily missed. Instances like these are where automated metadata tools are invaluable as they identify at-risk data within the system, but also can highlight its history. The transformations the data underwent can be observed allowing for insights such as what caused the data to be a threat, enabling the data governance team to act accordingly.

Protect your data with Alex

Once these steps have been undertaken correctly, an organization will have an enterprise level data protection framework. Governing the data manually or without adequate metadata tools is inefficient, inaccurate and risky. Businesses that deal with big data need to invest in solutions that are scalable to their enterprise.

With the metadata management tools Alex Solutions provides, a data protection framework can be implemented that is secure and built without disrupting business operations. Alex Cloud can easily migrate data from unrelated systems within an organization, ensuring all data is accounted for. The plethora of Alex’s connectors, can catalog structured and unstructured data, scan for sensitive elements within data and assign compliance controls. Alex uses heatmaps to autonomously scan enterprise data that discover data and systems at risk to exposure, notifying data owners when risk levels increase. Together with Alex, enterprises have established data protection systems that are holistically secure from mismanagement inside and attacks from outside.