Data Protector Overview

Secure Your
Data Directly
At The Source.

Native cloud security controls like encryption-at-rest or role-based access are useful but insufficient for protecting data at a granular level or when data is shared externally. The Protegrity Data Protection Framework enables you to apply the right data protection method to the right data, tailored to the diversity of data types in your environment—with flexible enforcement models aligned to the systems and environments you need to protect.
Apply Privacy Preservation anywhere in your architecture

Where Does Your Sensitive Data live?

Protect data in cloud-native platforms like Snowflake, BigQuery, Redshift, and other managed services. These protectors run inside the data platform itself to enforce policy without routing data externally—maintaining performance and compatibility with analytics, AI, and reporting tools.

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How Protectors work

Central Policy.

Local Enforcement.

Protectors integrate into existing systems and platforms — from applications to databases to cloud services — executing centrally managed policies to apply field-level security controls like tokenization, encryption, masking, hashing, or anonymization directly to sensitive data.

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Why fit-for-purpose data protection?

Native cloud security controls like encryption-at-rest or role-based access are useful but insufficient for protecting data at a granular level or when data is shared externally. Protegrity enables you to apply fit-for-purpose data protection methods to the diversity of data in your data project — with flexible enforcement models aligned to the systems and environments you need to protect

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Protegrity Platform Architecture

Built to Protect. Designed to share.

Secure data at rest, in transit, and in use across hybrid, multi-cloud, and AI environments. Built for data governance, discovery, protection and privacy.

Data Consumption

BI Tools Secure Analysis
AI/ML Models Predict, Learn, Automate
Business Apps Operational Decision Support
Data Marketplaces Internal or External Exchange
External Partners Secure Data Collaboration

Protegrity Data
Security Platform

Secure data where it lives, control how it’s used.

Data Sources

Cloud & Big Data
Databases
Data Warehouses
Apps & APIs
Files & Mainframes
Proven applications

POWERING CRUCIAL DATA
PROJECTS. PROTECTING CRITICAL DATA TYPES.

From cloud analytics to GenAI security, see how Protegrity enables business-critical data projects by enabling secure access to vital data.

Analytics
01
Analytics
Securely fuel BI dashboards and advanced analytics by protecting sensitive fields while maintaining data usability.
Cloud Security
02
Cloud Security
Enforce consistent data protection policies and visibility across AWS, Azure, GCP, and hybrid cloud environments.
Compliance
03
Compliance
Meet regulations like GDPR, HIPAA, and PCI DSS with policy-driven protection and automated audit-ready reporting.
Data Sharing
04
Data Sharing
Enable data access with partners or external apps using masking or tokenization to protect identifiers.
GenAI Security
05
GenAI Security
Minimize PII leakage risk in LLM outputs by discovering and redacting sensitive data before GenAI ingestion.
Business Intelligence
06
Business Intelligence
Enable global business intelligence by applying region-specific protection rules without delaying consolidated reporting.
Machine Learning
07
Machine Learning
Train effective ML models on safe, de-identified data using anonymization or synthetic data generation techniques.
App & Data Outsourcing
08
App & Data Outsourcing
Securely outsource app development or data processing by providing vendors with protected, de-identified data sets.
Data Marketplaces
09
Data Marketplaces
Create data products for monetization or sharing by de-identifying datasets while preserving utility and referential integrity.
Training & RAG (GenAI)
10
Training & RAG (GenAI)
Protect sensitive information in internal documents used for training custom RAG models and vector databases.

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