AUGMENT NATIVE SECURITY WITH DATA-CENTRIC PROTECTION.
GRANULAR PROTECTION WHERE NATIVE CONTROLS END.
NATIVE INTEGRATION. BROAD PLATFORM SUPPORT.
Protegrity provides native integrations and APIs that allow you to apply consistent, field-level protection across diverse cloud services, databases, data lakes, and data pipelines.
Native Platform Integration
Apply protection directly within the runtime of major cloud data platforms (Snowflake, Databricks, BigQuery, Redshift), ensuring security without requiring data movement or external proxies
- Seamless, high-performance integration with leading cloud data services
- Protection applied transparently during query execution or pipeline processing
- Maintains platform compatibility with existing analytics and AI/ML workflows
Broad Cloud
Service Coverage
Extend consistent protection beyond databases—including data lakes (S3, Blob, GCS), object storage, and critical data pipeline services across AWS, Azure, and GCP.
- Comprehensive support for major cloud providers and their key data services
- Includes protection for Hadoop ecosystems (Hive, Spark) running in the cloud
- Protect data within ETL/ELT services (Glue, Data Factory, Dataflow) via Cloud API
Field-Level
Protection Methods
Precisely enforce data protection policies, applying granular vaultless tokenization, encryption, masking, or anonymization to specific sensitive data fields within cloud environments.
- Protect PII, PCI, PHI, and other sensitive data types at the column or field level
- Protection methods operate transparently to users during query or data access
- Format-preserving options (i.e., dynamic data masking) maintain usability for downstream analytics and AI tools
Centralized Policy Enforcement
Ensure consistent application of enterprise-wide data protection rules. Policies are defined centrally in the Protegrity Enterprise Security Administrator (ESA) but applied locally by the Application Protector within the app context.
- All app-specific policies managed centrally via Protegrity ESA for consistency and simplified admin
- Local enforcement ensures security rules are always applied correctly in context
- Enables granular, policy-based control over who sees clear vs. protected data
APPLY PRIVACY PRESERVATION ANYWHERE IN YOUR ARCHITECTURE.
THE LATEST
FROM PROTEGRITY
Quantum Computing Meets AI: Where the Opportunity Is—and Isn’t
Quantum computing is often discussed in terms of the security challenges it could create for existing encryption. But as the technology advances, enterprises are also beginning to explore another question:…
Insider Risk After Access Is Granted: Reducing Sensitive Data Exposure
Insider risk is often framed as a question of who can get into a system. But many exposure scenarios begin after access has already been granted — when an employee,…
Enterprise AI Governance: CData Cites Protegrity Research
As enterprises connect AI agents to more operational systems and sensitive data, determining what those systems can access — and under whose permissions — is becoming an important part of…
Enterprise Data Security
In A Single Platform
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.
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