by BrainStream Chief User Experience Officer Yung-Wen Cheng
In BrainStream, protecting user data and maintaining database integrity are not just technical concerns—they are core responsibilities. We handle sensitive information such as student profiles, academic progress, login credentials, and sometimes even payment data. Ensuring that this information is secure, well-managed, and retained appropriately is crucial for both user trust and regulatory compliance.
Data Protection begins with strong encryption and secure access controls. All sensitive data should be encrypted both at rest and in transit using modern standards (such as AES-256 and TLS). Authentication mechanisms should include secure password handling, optional multi-factor authentication (MFA), and strict role-based access control (RBAC) for admin panels and internal tools. Cloud service providers like AWS, Azure, and Google Cloud offer built-in tools for managing these aspects securely.
Backups are vital for disaster recovery. Regular, automated backups should be scheduled—daily or even hourly depending on app activity—to safeguard against data loss from system failures or security breaches. These backups should be stored in secure, off-site or redundant cloud environments and tested periodically to ensure they can be restored quickly.
Data Cleaning and Retention also require deliberate planning. Not all data needs to be stored indefinitely. For example:
Short-term data (1–3 months): Login history, in-progress quizzes, and session logs can be purged or archived once no longer needed.
Medium-term data (6 months–1 year): Usage analytics or inactive user session metadata may be retained for trend analysis, then anonymized or deleted.
Long-term data (1–7 years): User profiles, certifications, academic records, and completed course data should be preserved, especially for compliance with academic or legal requirements. Institutions may dictate specific retention periods for this type of data.
Automatic data cleaning processes should be implemented to archive or delete unused or outdated data. Tools like cron jobs, cloud functions, or database lifecycle management features can be used to automate this process. Data that is no longer needed for active use but valuable for future insights can be anonymized and stored in data lakes or archives for future analysis.
Ultimately, a robust data protection and maintenance strategy supports trust, ensures compliance, and keeps the system fast and responsive. Responsible data stewardship is not just good practice—it’s part of delivering a high-quality, ethical learning experience.