EXTEND FIELD-LEVEL
DATA PROTECTION
TO LEGACY SYSTEMS.
PROTECT SENSITIVE DATA IN FILES & MAINFRAMES.
Broad integrations &
platform Support
File-Based
Systems
CSV, JSON, XML, Avro, Parquet, and other structured/semi-structured file formats
Mainframe
Environment
IBM z/OS, DB2 Mainframe,
COBOL applications
Legacy Apps & Batch Processing Systems
Custom scripts, ETL workflows, backups,
reporting exports
ENFORCE PRECISE PROTECTION WITHOUT DISRUPTION.
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
Batch & Integrated Processing
Apply protection in bulk via scheduled batch jobs—or integrate protection steps directly into existing data transfer or processing workflows as needed.
- Supports offline processing suitable for large files or established batch windows
- Can be scripted for automated, repeatable file protection tasks within workflows
- Effective in air-gapped or high-control operational data center environments
Centralized Policy Enforcement
Define data protection policies once in Protegrity Enterprise Security Administrator (ESA) and apply them consistently to files and mainframe data alongside databases, applications, and cloud data sources.
- All app-specific policies managed centrally via Protegrity ESA for consistency and simplified admin
- Simplifies governance and auditing for complex, hybrid IT environments
- 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