Protegrity Data Protection Platform

The Protegrity Data Protection Platform manages securing sensitive data across diverse and dynamic cloud environments. This platform is designed to protect data at rest, in motion, and during use, ensuring compliance and security throughout the data lifecycle.

Technically, Protegrity employs a centralized policy management approach, allowing for fine-tuned data protection at a field level based on data sensitivity and usage. The platform utilizes various protection methods, including tokenization, encryption, dynamic data masking, and anonymization. These methods are applied locally, ensuring that data remains protected whether it is on-premise, in the cloud, or in transit. The use of vaultless tokenization, for instance, facilitates deterministic matching for analytics purposes while maintaining high throughput and scalability.

Operationally, Protegrity integrates with multiple cloud service providers such as AWS, GCP, and Azure, as well as on-premise and hybrid environments. This integration is supported by native platform integrations, performance optimization for each platform, and role-based access control to segregate security duties from systems administration. The platform also includes independent audit logs and centralized monitoring, providing a "single pane of glass" for visibility and control over data protection.

Key operational considerations include the need for thorough data discovery and classification, which Protegrity facilitates through its tools and partnerships. The implementation process involves establishing data flow patterns and placing embeddable data protection at optimal points in the enterprise architecture. Additionally, the platform's scalability and elasticity are crucial, as it must handle sustained and bursty loads across containers, serverless environments, and virtual machines.

From a technical details perspective, Protegrity's data protection methods are highly customizable, allowing for the combination of different protection techniques to match the sensitivity of the data. The platform supports various deployment models, including public, niche, and industry-specific clouds, and it is designed to be DevOps-ready with features like application protectors and data security gateways.