How Sensitive Data Discovery Helps Protect Your Student Data


How Sensitive Data Discovery Helps Protect Your Student Data



If you work in the educational sector, whether as a university professor, college administrator, a tutor or teacher, or even a high school principal, you will no doubt come into contact with student data. But did you know that the collection, handling, and privacy of student data is subject to the requirements of the Family Educational Rights and Privacy Act (FERPA)?

Don’t worry. You’re in the right place. In this blog, we’ll unpack everything you need to know about the requirements of FERPA as well as how to leverage technology for student data protection.

What is FERPA?

FERPA is a data privacy law that sets forth the basic privacy requirements education entities must adhere to for student data protection.

Under the provisions of FERPA, educators may not disclose student data to anyone without explicit consent. The act also grants certain rights to parents as well as students who are eighteen or older. Parents have the right to request their children's education records, and this right also extends to any student over the age of eighteen. A school must accommodate these requests within 45 days.

FERPA applies to any public or private elementary, secondary, or post-secondary school and any state or local education agency that receives federal funds.


What makes student data sensitive?


Under FERPA, student data falls into two categories, namely personally identifiable information (PII) or directory information. PII is any data that can be used to identify an individual student such as name, address, social security number, and student number. Directory information is any student data that would be found in an educational record, such as grades, religious beliefs, and medical history. 


Both PII and directory information should be considered sensitive data. In the wrong hands, this information can be used to directly or indirectly identify a student and place them in harm’s way. This information can also be used to commit fraud or blackmail or even sold on the dark web. 


How at risk is student data?


Unsecured student data is vulnerable to cybercrime, data breaches, and malware attacks. According to recent analysis by a federal watchdog, thousands of K-12 students were affected by nearly one hundred reported data breaches over the last four years.


With more and more students studying from home to curb the spread of COVID-19, the risk of a potential breach has only increased. Technology tools and apps are making it possible for educators and students to connect from anywhere, but the opposite is also true. More channels for data flow means a higher risk of a data breach taking place. 


How can Sensitive Data Discovery help?

Sensitive Data Discovery is a method of data protection that automatically locates and classifies sensitive information in your organization, allowing you to create an inventory of sensitive data from which to center any remedial action. This form of data protection is a necessary first step for being compliant with the requirements of FERPA, which has strict requirements for student data protection. 


Before you can mask or encrypt data, you first need to understand where that data is located. Most educational institutions store large amounts of data about both past and present students in databases containing thousands of tables of both direct and indirect identifiers. Using expert determination algorithms like those found in sensitive data discovery tools is the best technique to ensure optimum data protection and compliance. Once you've located this sensitive data, you can use another trusted method such as data masking to protect the confidentiality of student data. 


Sensitive data discovery allows you to manage three key elements of successful student data protection, namely FERPA compliance, student data privacy, and data loss prevention. Hush-Hush Sensitive Data Discovery uses multiple patented algorithms to fine-tune your data protection strategy and ensure the best match. 


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