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
Frontier Model Security: Why AI Governance Needs Stronger Guardrails
AI governance is moving from policy discussion to security reality. As frontier models become more capable and more deeply connected to enterprise systems, the question is no longer only how…
Protegrity Shares Perspective on Scam Awareness and Protecting Personal Data
Consumer scams are becoming harder to recognize because they often begin with details that feel familiar: a name, a workplace, a relationship, or information pulled from public records, social media,…
Protegrity on OpenAI Daybreak and AI-Driven Vulnerability Discovery
AI is beginning to change not just how vulnerabilities are found, but how quickly they can be tested, prioritized, and acted on. In a recent Cyber Defense Magazine post, the…
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