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
How to Make Security Developer-First in the Gen-AI Era
The external piece, “Designing Security for Developers, Not Around Them” (Oct 16, 2025), makes the case that as Generative AI (GenAI) accelerates developer productivity, security must shift from perimeter-centric models…
Smarter Systems Safer Data – Key Insights From Our Latest Security Perspective
The external piece argues that compliance alone does not equal security and that organizations should simplify architectures, push protections closer to the data, and adopt proactive defenses. Below is a…
CORRECTING and REPLACING Protegrity Releases Free Developer Edition on GitHub for GenAI Privacy Innovation
Protegrity announced the free Developer Edition, a lightweight, containerized toolkit aimed at helping developers, data scientists, and security practitioners embed data protection and GenAI guardrails into Python workflows without standing…
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