The main technologies for data leakage prevention (DLP) include:
Data Discovery and Classification: Identifying and categorizing sensitive data across systems to apply appropriate protection policies. For example, scanning databases and file servers to locate personally identifiable information (PII) or financial records.
Example: A company uses automated tools to classify customer data stored in cloud storage as "Highly Sensitive" and enforces encryption.
Policy-Based Enforcement: Defining rules to control data access, sharing, and transfer based on sensitivity levels.
Example: Blocking employees from emailing spreadsheets containing credit card numbers outside the organization.
Endpoint DLP: Monitoring and controlling data movement on devices like laptops and USB drives to prevent unauthorized copying or transfers.
Example: Preventing a user from copying sensitive files to an external drive without approval.
Network DLP: Inspecting data in transit across networks to detect and block leaks via email, web, or other channels.
Example: Intercepting an attempt to upload confidential files to a personal cloud storage service.
Cloud DLP: Extending protection to data stored or processed in cloud environments, ensuring compliance with security policies.
Example: Using Tencent Cloud’s Data Security Center to monitor and encrypt sensitive data in COS (Cloud Object Storage).
User Behavior Analytics (UBA): Detecting anomalies in user activity that may indicate insider threats or compromised accounts.
Example: Flagging unusual login attempts from a foreign IP address to access sensitive databases.
Encryption and Tokenization: Securing data at rest and in transit to render it unusable if leaked.
Example: Encrypting database fields containing health records using Tencent Cloud’s Key Management Service (KMS).
For cloud-based DLP, Tencent Cloud offers integrated solutions like Data Security Center and KMS to help businesses safeguard sensitive data across hybrid environments.