Data security refers to the protection of data from threats like unauthorized access, breaches, or cyber-attacks. The goal is to keep data secure in all forms—whether it’s at rest (stored data), in transit (data moving across networks), or in use (being actively processed).

🛡️ Core Principles of Data Security (CIA Triad):

1. Confidentiality – Ensuring that only authorized individuals or systems can access sensitive data.

2. Integrity – Ensuring that data remains accurate, unaltered, and complete during storage and transfer.

3. Availability – Ensuring that data is accessible and usable when needed by authorized users.

🔑 Key Data Security Threats:

• Data Breaches: Unauthorized access to sensitive data, often caused by hacking or insider threats.

• Data Loss: Accidental or deliberate deletion or corruption of data.

• Ransomware: Malware that encrypts data and demands a ransom for its release.

• Phishing: Deceptive attacks aimed at gaining unauthorized access to data by tricking users into revealing credentials.

• Insider Threats: Employees or other trusted individuals deliberately or accidentally compromising data security.

• Man-in-the-Middle Attacks: Interceptions of data as it travels over unsecured networks.

🛠️ Key Data Security Measures:

1. Encryption – Converts data into unreadable text for unauthorized users. This is crucial for protecting data at rest (stored) and in transit (being transferred across networks).

   • At Rest: Encrypting stored data (e.g., on hard drives, cloud storage).

   • In Transit: Using protocols like HTTPS, TLS, and VPNs to encrypt data as it moves.

2. Access Control – Restricting access to data based on user roles and the principle of least privilege (only give users access to data they need to do their jobs).

   • Role-Based Access Control (RBAC)

   • Multi-factor Authentication (MFA) for added security.

3. Data Masking – Hiding sensitive data in a way that it cannot be viewed or accessed without proper authorization, often used in development and testing environments.

4. Backup and Recovery – Ensures that in the event of data loss, copies of the data are available to restore.

   • Implement regular, encrypted backups.

   • Test backup and recovery processes periodically.

5. Data Minimization – Limiting the collection of data to only what is necessary, and purging old or unnecessary data to reduce exposure.

6. Data Anonymization – Replacing identifiable data with pseudonyms to protect privacy while still allowing for analysis and processing.