USE SENSITIVE DATA WITHOUT EXPOSING INDIVIDUALS.
PRIVACY ENGINEERING FOR CONFIDENT DATA USE
Anonymize sensitive data while preserving analytical value with Protegrity’s Anon solution. Deploy across cloud or on-prem environments to support AI, analytics, and compliance needs.
Retention Expiration
Anonymize aged customer or transaction records to meet retention policies while preserving historical patterns that are crucial to use cases like fraud analytics or risk modeling.
Privacy-First AI/ML
Prepare privacy-safe AI training datasets by anonymizing sensitive attributes before model ingestion, maintaining performance while minimizing privacy risks like memorization.
Secure Internal Data Sharing
Confidently share behaviors or analytics datasets between internal teams or departments by removing direct identifiers to prevent accidental privacy breaches.
Healthcare Analytics
Enable HIPAA-compliant cohort analytics, clinical research, and outcome studies by applying advanced anonymization techniques to sensitive patient data.
Cloud Data Lake Protection
Apply anonymization before writing to cloud storage (e.g., S3, Azure Blog) to prevent sensitive data exposure in data lakes.
Selective Operational Anonymization
Target and anonymize specific records within operational databases (e.g., based on user consent status or data expiration) while leaving active records untouched.
ADVANCED PRIVACY PRESERVATION TECHNOLOGIES
APPLY PROTECTION ANYWHERE IN YOUR ARCHITECTURE
Protectors
based
SDKs
Utilities
THE LATEST
FROM PROTEGRITY
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Protecting sensitive enterprise data requires more than limiting access. Organizations also need to preserve the ability to analyze and process information across their existing data environments without unnecessarily exposing sensitive…
CharliAI and Protegrity Partner to Fast-Track AI Agents Stuck in Risk Review Into Production
Protect your data. Control your agents. Prove your outcomes. Built to withstand the scrutiny regulated industries face with AI. VANCOUVER, BC and FAIRFIELD, CT: October 20, 2026. CharliAI and Protegrity…
Cyber Press Highlights Protegrity in 2026 Tokenization Guide
Protecting sensitive data is only part of the challenge for enterprise organizations. Data teams also need to maintain the ability to analyze and process information across warehouses, cloud platforms, and…
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