Data protection designed for data consumption.
Balance security needs with data utility.
Protegrity provides the most complete range of data protection methods, enabling organizations to develop fit-for-purpose data protection strategies that meet their most pressing data security challenges.
Field-Level Protection In The Cloud
Tokenize or mask sensitive customer data (PII, PCI, etc.) stored in platforms like Snowflake, BigQuery, or Redshift, while preserving usability for reporting, AI, and analytics.
De-Identification
Anonymize or pseudonymize sensitive datasets (like patient or customer data) to enable secure and compliant research, analytics, and ML model development.
Role-Based Masking
Dynamically mask or redact sensitive fields (e.g., payment info, account numbers) based on user role or session context within internal applications or BI tools.
Synthetic Data
Generate statistically realistic but artificial datasets for testing applications or training AI/ML pipelines when real production data cannot be used due to privacy or legal restrictions.
Cross-Border Data Tokenization
Apply region-specific tokenization or other protection methods to meet data localization requirements like GDPR while enabling consistent global operations and reporting.
Proxy-Based Protection for Legacy Systems
Secure sensitive data flowing to or from legacy applications and systems using proxy-based protectors (like DSG) without requiring complex or risky modifications to the original application code.
Protection Methods for diverse data environments.
APPLY PROTECTION ANYWHERE IN YOUR ARCHITECTURE
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THE LATEST
FROM PROTEGRITY
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Quantum readiness is becoming a planning issue long before quantum computing reaches full maturity. Organizations can start now by understanding where cryptographic exposure exists, prioritizing the systems that matter most,…
Protegrity’s Greg Stout on Why Sensitive Data Can Stall AI Pilots
An AI proof of concept can work well in a controlled environment and still run into serious obstacles on the way to production. In a recent article for The AI…
Bringing AI Governance and Data Protection Into the Developer Workflow
AI development moves fast, but sensitive data still needs to be understood and protected before an application reaches production. For developers, that means knowing where sensitive information appears, how it…
ENTERPRISE DATA SECURITY
IN A SINGLE PLATFORM
Discovery
Identify sensitive data (PII, PHI, PCI, IP) across structured and unstructured sources using ML and rule-based classification.
Learn moreGovernance
Define and manage access and protection policies based on role, region, or data type—centrally enforced and audited across systems.
Learn moreProtection
Apply field-level protection methods—like tokenization, encryption, or masking—through enforcement points such as native integrations, proxies, or SDKs.
Learn MorePrivacy
Support analytics and AI by removing or transforming identifiers using anonymization, pseudonymization, or synthetic data generation—balancing privacy with utility.
Learn more