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Guidelines for Managing Sensitive Data in Higher Education
Redaction for higher education

Universities and colleges deal with a lot of data, from student records to research materials. They also need a lot of data for learning and research purposes. While not all data shared with higher education institutions can be considered private, there’s still a special category specified as sensitive data.


Sensitive data is any information that should be kept secret or private. Exposing such information could lead to serious security breaches and possible legal consequences. This data should be stored well and protected from unauthorized use whether inside or outside the institution.


Sensitive data in higher education includes research materials subject to confidentiality agreements. They also include student records and data protected under regulatory bodies such as FERPA (Family Educational Rights and Privacy Act) and GDPR (General Data Protection Regulation). These institutions must safeguard the data to maintain their integrity and protect students’ rights to privacy.


But how can they manage search sensitive data and uphold their integrity? This guide explores ways higher education can manage sensitive data and maintain a secure learning environment for their students.

Definition of Sensitive Data

Not every data that falls in the hands of higher education management is considered sensitive. Sensitive data in higher education can be categorized into three:

1. Student records (protected by FERPA or equivalent)

The Family Educational Rights and Privacy Act (FERPA) is a federal law mandates all higher education institutions to preserve students' education records and ensure they can get these records whenever they need them. These records include name of the students, family information performance, transcripts, financial aid, disciplinary records, etc.


To comply with FERPA, these institutions must implement student data security. They should have security controls in place to preserve the confidentiality of the data and restrict data from unauthorized access.


This information can only be shared with authorized parties or when they obtain consent from the students. FERPA also ensures that the use of AI in generative and
predictive inputs in higher education does not result in the disclosure of protected data. So, these institutions must be careful when using AI in education. Techniques such as redaction can protect hide sensitive information when using AI for data analysis.

2. Research data subject to confidentiality agreements

Many times, higher education institutions engage in research where they collect all kinds of data from participants. This data may include names of participants, medical information, interviews, and survey responses. The research results, unpublished concepts, trade secrets, and any other proprietary data may also be subjected to a confidentiality agreement because the data is extremely sensitive.


The institutions and their researchers are bound by law to protect this data by signing confidentiality agreements with all the parties involved. Confidentiality agreements are crucial to protect the privacy of participants and provide strong assurance that the information will remain private unless there’s consent or authorization for release to the public.


Any party that releases these data after signing the confidentiality agreements can face legal consequences. So, if universities end up with data subject to confidentiality agreements, they must keep it private by employing stringent security measures.

3. Personal data under GDPR or other privacy laws

The globalization of education has allowed universities to collect data from individuals and students outside their home countries. Higher education institutions must comply with GDPR (General Data Protection Regulation) to protect any data entrusted to them by these individuals.


If colleges and universities collect data from anyone living in the European Union (EU), including their students, alumni, distance learning students, donors, or vendors, they must protect the data under GDPR regulation. This data must be protected at every step, including collection, structuring, organization, storage, communication, destruction, etc. GDPR has made great strides in improving data protection for individuals. It ensures higher education provides maximum security against data breaches. It also ensures a higher quality of data collection with stronger standards that make everyone aware of data protection.

However, the regulation is still a work in progress and many institutions are still struggling to grasp the concept.

Mandatory Data Sanitization

Due to increased risk of data breaches and security threats, data sanitization has become really important. Sanitization safeguards data privacy to ensure it meets the compliance laws and regulations.


The ever-evolving storage domain, technology, and the increased use of AI have forced higher education institutions to increase their vigilance when it comes to data storage and usage. These institutions are forced to adopt more stringent measures to ensure data privacy.  They are legally obliged to protect data and maintain compliance.


Data sanitization is the process of undertaking data destruction in a secure, deliberate, and irreversible manner. Data that has been sanitized should not be recovered by any means. The individuals mentioned in the data must not be identified either by human or AI once the data has been sanitized.

Importance of Data Sanitization

Data sanitization helps in the following ways:


·    Ensures compliance: Data sanitization is one way higher education can comply with data privacy laws and regulations. The process gives customers of these institutions the right to be forgotten under 17 of GDPR. The institutions are mandated to erase their customer data from their systems completely.


·    Enhanced privacy: While data can remain
private, this is not always a guarantee as long as the information is still stored. Data sanitization is the only assurance that this data will be completely forgotten and won’t resurface in the future.


·    Reduced data footprint: Sanitization reduces data footprint under the organization’s realm. This reduces the possibility of attack from all points while mitigating the risks associated with data management.

Why It’s Important to Redact Sensitive Data Before Sharing with AI Tools

Thanks to technology, universities are constantly utilizing AI to collect data. Tasks such as research analysis, personalized learning, and admission processing have all been left to AI. However, leaving AI to handle sensitive data brings significant risks that can expose the institution to serious legal consequences.  To mitigate the risks, these universities are required to redact sensitive information before sharing their data with AI.


Data redaction is defined as the process of censoring or hiding sensitive information from a document or file. The method helps in hiding copyrighted information, confidential data, medical information, trade secrets, and intellectual property. Some of the reasons universities should redact data before sharing it with AI include:


·    Ease of data sharing: It’s easier to share redacted data without worrying about exposing any sensitive information. You have the freedom to share with third parties of researchers without worrying about leakage.


·    Data protection: Redacting data is a way of protecting information from authorized persons. It protects data from breaches and unauthorized use.


·    Compliance: Redaction ensures universities remain compliant with GDPR and FERPA regulations. The institutions practice their mandate of protecting their customer data and complying with the law.

Using Approved Tools for Data Redaction

For effective data redaction, universities must use approved tools. Before choosing a tool, you must understand the type of data you’re dealing with, how much information you need to hide, and how you want the software to handle the work.


There are different types of data redaction tools, which can blur, delete, hide, encrypt, replace, or hash information. For example, you can use PII redaction software if you want to redact personal identifiers from PDF software. The software can identify names, numbers, SSNs, and addresses from such documents.


If you want to blur sensitive information from a document, iDox.ai should be your ideal tool for the job. The tool is capable of identifying logos, numbers, or faces from the document and blur them. iDox tool uses AI to identify information that needs to be blurred based on prompts and inputs you feed it. The tool employs its advanced technique to anonymize data while still maintaining the integrity of the information in the document.


Apart from using the approved tools for data redaction, universities have the option of using custom APIs. The custom APIs are made so that they integrate seamlessly with existing data systems for easier redaction. The APIs can identify and redact sensitive information by relying on the use case and depending on the specific AI tool being used.


Conclusion

Continuous technological evolutions and the use of AI have made life easy for educational institutions. Due to technology, data collection, processing, and management have become easy. However, there is also the risk of data leakage, breaches, and security issues when data utilization is involved.


Therefore, these institutions must comply with FERPA and GDPR regulations to uphold their integrity and keep student data private. They should strive to achieve a secure learning environment where students feel that their privacy is protected at all costs during their stay in the institution and when they finally graduate.


Data sanitization and redaction using approved tools are some of the ways these universities ensure data does not end up in the wrong hands. Tools such as iDox.ai
help keep sensitive information provided by blurring them so that they’re not seen by other people. Data literacy and privacy awareness will also help these
institutions keep their data safe and avoid getting in trouble with the law.


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