Equally important as classifying EAR Sensitive data, is categorizing it. Once data is deemed as sensitive or non-sensitive, an organization must apply a set of categories that will differentiate data based on confidentiality and authorization. While it’s understood that an inherent rule of EAR compliance demands that EAR Sensitive data not be seen by non-US citizens, data classification can take this rule a step further and apply authorization internally. On a rudimentary level, data can be classified in ways so that non-US citizens within the organization are not allowed access. Furthermore, this data can be classified as a means to provide internal confidentiality. Technical data can be grouped in different confidentiality levels, for example; 1) Public Use 2) Internal use 3) Confidential 4) Top Secret, and so forth. This can provide appropriate authorization both inside and outside the organizati
EAR Compliance and Data Classification Pt. II; Categorization
After data has been properly classified, as a result of a successful Data Classification deployment, proper protection can be applied as a means to make sure that data remains protected and is only accessed appropriately. There are several data security solutions that can provide adequate protection in an EAR context. Solutions that protect against unauthorized access would likely be the optimal choice for companies looking to protect EAR sensitive data. For example, “Multi Factor Authentication” can protect against hackers, as both a physical token and a password are required to access, making it virtually impossible for hackers to break through. “Identity and Access Management” can also provide proper access controls, especially in an internal environment. IAM gives users certain restrictions in accordance to data access, making sure that confidential data is not seen by the wrong employees.
Although EAR Compliance is a long and comprehensive undertaking, especially when securing data, it is important to adhere to compliance requirements so that your organization can avoid potentially bankrupting fines. With a well planned and executed Data Classification deployment, much of the initial heavy lifting can be taken care of, making for a much easier implementation of Data Security.