FIELD-LEVEL DATA PROTECTION FOR DATA
AT REST & IN USE.
PROTECT SENSITIVE DATA AT THE DATABASE LAYER.
ENFORCE PRECISE PROTECTION WITHOUT DISRUPTION.
Format-Preserving Protection Methods
Apply protection methods (like format-preserving tokenization) that maintain the original data format, ensuring compatibility and usability within SaaS application fields and workflows.
- Allows protected data (e.g., tokenized credit cards) to work within SaaS processes
- Avoids breaking SaaS application field validation or formatting rules
- Balances strong data security with critical SaaS operational requirements
Background Operation
Works without requiring changes to existing applications, SQL queries, reporting tools, or database schemas, reducing impact on users and dependent systems.
- Protection applied automatically during data access or query execution
- No changes needed for end-users or connected applications
- Reduces setup time and effort for adding data protection to existing stores
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.
- 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 Enterprises Can Build a Practical Quantum Readiness Plan
Quantum readiness is becoming a planning issue long before quantum computing reaches full maturity. Organizations can start now by understanding where cryptographic exposure exists, prioritizing the systems that matter most,…
Protegrity’s Greg Stout on Why Sensitive Data Can Stall AI Pilots
An AI proof of concept can work well in a controlled environment and still run into serious obstacles on the way to production. In a recent article for The AI…
Bringing AI Governance and Data Protection Into the Developer Workflow
AI development moves fast, but sensitive data still needs to be understood and protected before an application reaches production. For developers, that means knowing where sensitive information appears, how it…
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