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.