Summary:
Google's Sensitive Data Protection, part of its Data Loss Prevention (DLP) solution, is a comprehensive tool designed to discover, classify, and protect sensitive information across Google Cloud services. It helps organizations in preventing the exposure of critical data such as personally identifiable information (PII), financial details, or any confidential business information. By leveraging advanced data analysis and machine learning, this solution can detect and redact sensitive data in various formats, both in motion and at rest, across a wide range of Google Cloud environments. It is particularly valuable for businesses that handle sensitive information and need to comply with data protection regulations while leveraging cloud technology.
Key Features:
- Automatic Data Discovery and Classification: Identifies and classifies sensitive data within cloud storage and data streams.
- Customizable Detection Rules: Allows for the creation of custom detectors to identify specific types of sensitive data according to business needs.
- De-identification and Redaction: Offers various options to de-identify sensitive data through redaction, masking, or other transformation methods.
- Risk Analysis and Reporting: Provides comprehensive risk assessments and reporting capabilities to understand data exposure and compliance risks.
- Integration with Google Cloud Services: Seamlessly integrates with various Google Cloud products for a unified approach to data protection.
Partner Link:
Tangible Benefits:
- Reduction in Data Breaches and Compliance Violations:
- Quantification Path: Measure the frequency of data breaches involving sensitive information and the incidence of compliance violations before and after implementing the DLP solution. Assess the reduction in such incidents to quantify improvements in data protection.
- Cost Savings in Data Protection and Compliance Efforts:
- Quantification Path: Evaluate the cost savings from streamlined data protection processes, including reduced need for manual data handling and lowered risk of compliance fines.
- Operational Efficiency in Handling Sensitive Data:
- Quantification Path: Assess the time and resources saved by automating the discovery, classification, and protection of sensitive data. Quantify the reduction in manual efforts and operational overhead.