Data Privacy Compliance Technologies for Higher Education
Higher education institutions (HEIs) are accountable for managing vast amounts of sensitive data, ranging from personal health and financial information to government identification records. As data custodians, HEIs face increasing accountability and responsibility in safeguarding this valuable information.
HEIs of all sizes, including community colleges, liberal arts colleges, and universities, grapple with common regulatory mandates requiring robust data governance. As regulations and best practices emerge, higher education becomes an increasingly critical sector to reinforce with the necessary structures, technologies, and policies that can support compliance with regulatory mandates protecting students, staff, and faculty.
Technologies
Data Governance Platforms
Higher education institutions can benefit from implementing data governance and privacy management platforms that provide a centralized hub for managing privacy policies, technologies, and compliance status. These platforms enable HEIs to establish and enforce consistent privacy practices across departments and systems. For instance, a data governance platform can help institutions create and maintain a data inventory, define data classification levels, and track consent management for different data processing activities. It can also facilitate privacy impact assessments and monitor compliance with complex regulations such as GDPR.
Encryption
Encryption lies at the center of data privacy. In compliance with regulations, HEIs must implement encryption techniques to secure data at rest and in transit. For instance, encryption can be applied to databases containing student records, financial data, or research findings, and to communication channels such as email or file transfers. By encrypting data, HEIs minimize the risk of unauthorized access, ensuring that even if the data is compromised, it remains unreadable to unauthorized individuals.
The AES-256 cipher, developed for the United States Government, remains the gold standard in encryption for use cases ranging from personal to military purposes. An all-encompassing privacy initiative must not only implement a similar standard, but continuously monitor and evaluate its effectiveness using a centralized governance platform.
Access Control
Access control mechanisms based on user roles are essential in higher education to limit data access to authorized individuals. HEIs can implement role-based access control (RBAC) systems to manage user permissions and restrict access to sensitive information. For example, student data should only be accessible to faculty members or staff with legitimate educational or administrative needs. RBAC systems can assign roles with specific access privileges, allowing only authorized individuals to view, edit, or delete data. Implementing access control measures helps prevent data breaches, unauthorized disclosures, and insider threats.
Data Masking & Anonymization
In compliance with any open records acts or when sharing data for research purposes, HEIs can utilize data masking or anonymization techniques to protect privacy. Data masking involves replacing sensitive information with fictional or altered values, ensuring that the data remains useful for analysis or testing while preventing the identification of individuals. Anonymization techniques, on the other hand, remove or modify personally identifiable information from datasets, rendering it impossible to re-identify individuals. By employing data masking and anonymization, institutions can balance the need for data utility with privacy protection, ensuring compliance with regulations and maintaining confidentiality while harnessing the power of their data.
Approaches
Due to the specificity of the educational use case, HEIs are uniquely subject to additional regulation and scrutiny which should be accounted for in governance measures. A robust implementation is more than the sum of its parts, requiring supportive and reinforcing interactions between technologies.
Data Classification
Assigning classifications to datasets aids the effectiveness of data privacy efforts by facilitating access control. A HEI, for example, can implement a data classification framework to categorize datasets based on their sensitivity and confidentiality levels. This allows the institution to apply appropriate security measures and access controls to ensure that data is only accessible to authorized individuals.
The data classification framework can include different levels such as:
1. Public: This category includes data that is publicly available and does not contain any sensitive or confidential information. It can be accessed by anyone without any restrictions.
2. Internal: This category includes data that is meant for internal use within the institution. It may contain sensitive information but does not pose a significant risk if accessed by authorized personnel. Access to this data should be restricted to employees and staff who require it for their job roles.
3. Confidential: This category includes highly sensitive data that, if accessed by unauthorized individuals, could result in significant harm or breaches of privacy. Examples include personally identifiable information (PII), financial records, health information, and research data. Access to this data should be strictly controlled and limited to individuals with a legitimate need and appropriate authorization.
4. Restricted: This category includes data that is highly sensitive and requires the highest level of protection. It may include data subject to legal or regulatory restrictions, trade secrets, or intellectual property. Access to this data should be granted only to a select few individuals who have specific permissions and undergo additional scrutiny.
Multi-Jurisdiction Compliance
With their broad data scope, HEIs are responsible for maintaining compliance across data disciplines and jurisdictions. Managing the overlaps and nuances of each applicable regulation becomes quickly overwhelming, necessitating an integrated compliance platform such as Informatica, OneTrust, TrustArc, or Securiti. Regionalized notices, privacy policies, and cookie consent, for example, are critical to maintaining good standing across jurisdictions in addition to governance efforts.
Data Subject Access Request (DSAR) Processes
Legislation like GDPR often requires the availability of processes facilitating data access requests by data subjects. Similar processes are found in the UK’s Freedom of Information Act, and certain U.S. state laws, with varying degrees of anonymization necessary.
In fostering a multi-jurisdictional data culture, access is critical to consider as it involves a deliberate outflow of potentially sensitive data. Processes must be established to triage requests, verify requesters’ identities as necessary, and fulfill or refuse requests. A Data Protection Officer can be appointed to oversee requests involving data of special categories, for instance in the event a student requests health records held by the institution.
Compliance technologies, when implemented effectively and tailored to the specific needs of higher education institutions, provide a solid foundation for safeguarding data privacy, meeting regulatory requirements, and instilling confidence among stakeholders. HEIs must assess their unique data privacy risks and build a platform of technologies to create a customized and comprehensive privacy framework.
To plan your Data Privacy initiative, read more at: https://www.inceptds.com/data-privacy-assessment-strategy