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
From Awareness to AI-Powered Action: What Cybersecurity Needs Now
The external piece, “AI Is Redefining Cybersecurity: Why Awareness Alone No Longer Protects Us” (Oct 29, 2025), spotlights a simple shift: awareness is table stakes; action and AI-powered resilience are…
Building a Cyber Strong Industry: From Awareness to Action
Published summary based on a third-party press release • Last updated: Oct 27, 2025 The external piece, “Building a Cyber Strong Industry: From Awareness to Action,” spotlights how this year’s…
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…
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