Protegrity data protection platform

Remove Security Friction From Data Projects.

Protect sensitive data with centralized policies and persistent, field-level controls—so teams can safely use data across analytics, AI, and operations without sacrificing security or compliance.
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

Enterprise Data Security
In A Single Platform

Equip teams to discover, govern, and protect sensitive data across
the data lifecycle—including for analytics and AI.

Discovery

Identify sensitive data (PII, PHI, PCI, IP) across structured and unstructured sources using ML and rule-based classification.

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Governance

Define and manage access and protection policies based on role, region, or data type—centrally enforced and audited across systems.

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Protection

Apply field-level protection methods—like tokenization, encryption, or masking—through enforcement points such as native integrations, proxies, or SDKs.

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Privacy

Support analytics and AI by removing or transforming identifiers using anonymization, pseudonymization, or synthetic data generation—balancing privacy with utility.

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What do you want to protect?

PROTECTION THAT MATCHES YOUR STACK

One-size-fits-all data protection limits data projects from the start. Protegrity lets you choose data protection tailored to the specific architecture and use case of any data project. — across cloud platforms, applications, databases, mainframes, and files — all managed from a central policy engine.

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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PCI Readiness Survey: Key challenges for 2025 

PCI Readiness Survey: Key challenges for 2025 

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Integration Partners

Amplify Native Data Security. Get More Value From Data Platforms.

Protegrity seamlessly integrates with leading data platforms, plugging gaps in native data security tools and giving you smarter data protection — so you can do more with your data and get more out of your investment.

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Deploy via AWS Marketplace
Integrate protection into S3, Redshift, EMR, and RDS
Use IAM roles, Lambda, and EKS for scalable enforcement
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Protect data in Synapse, Blob Storage, and SQL DB
Use Azure-native policy connectors
Leverage AKS support for containerized deployment
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Tokenize or redact structured/unstructured data in Delta Lake
Integrate with Spark pipelines
Enforce protection pre-analytics or ML ingestion
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Tokenize and mask fields in Snowflake
Integrated classification/discovery for Snowflake Data
Support Snowpark and secure data sharing
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Apply policy-based protection across distributed SQL environments
Mask/tokenize across federated data in multi-cloud settings
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Tokenize/mask in Hive/Impala
Support legacy Hadoop deployments
Use across hybrid structured data environments
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““Protecting our customer’s PII data is essential. Protegrity tokenization accelerates our secure transformation while enabling advanced 
analytics with protected data.” “

Steve Etchelecu
Solution Architect
Why Protegrity

Data-centric security designed for modern data projects

Protegrity simplifies agile data protection by removing limits—covering all your data types, integrating with all your platforms, and freeing you to match our broad range of advanced protection methods to the specific risk and demands of each data project.

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Field-level security

Protect the data itself

Secure the data element, not just the system. Control exactly who sees what with field-level tokenization, encryption, or masking, ensuring protection follows the data.

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format-preserving protection

maintain data utility & referential integrity

Keep protected data usable for reports, dashboards, and AI. Format-preserving protection methods let data teams run queries and train models without exposing sensitive information.

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Centralized policy management

ensure continuous compliance

Simplify compliance across complex environments. Centrally create and manage policies, monitor all access, and automate audit logging for consistent enforcement and proof.

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cross-cloud compatibility

Build future-ready data protection

Protect data uniformly wherever it lives—AWS, Azure, Snowflake & more. Centralized control across clouds reduces complexity and ensures consistent security, now and for the future.

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containerized deployment & native api support

Accelerate business-critical data projects

Speed up development cycles with easy integration. Embed protection via lightweight APIs, SDKs, or containers (Docker/K8s), empowering developers to build securely, faster.

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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