EMBED DATA
PROTECTION DIRECTLY INTO Applications.
SECURE DATA AT THE POINT OF ENTRY OR USE.
ROBUST PROTECTION. FLEXIBLE DEPLOYMENT.
Protegrity comes with a complete deployment toolset and pre-built connectors to deploy field-level data protection within diverse applications and enforce central policies across your data flows.
Direct SDK Integration
Embed protection logic directly into your application codebase using native SDKs for popular programming languages to ensure efficient, in-process security.
- Available for Java, .NET, Python, Golang, C, and others
- Supports protection at data entry, internal processing, or storage
- Operates locally within the application environment for optimized performance
API-Based Protection (Cloud API)
Use REST APIs to apply consistent, policy-driven protection within serverless functions, containerized applications, or cloud-native services like data pipelines (AWS Glue, Azure Data Factory).
- Ideal for microservices, serverless, and modern cloud-native architectures
- Apply protection dynamically per API request based on context or policy
- Extends consistent protection beyond traditional application boundaries
Field-Level Protection Methods
Apply protection methods—including advanced vaultless tokenization, encryption, masking, or anonymization—to specific sensitive data fields within data structured payloads or memory.
- Targets only sensitive fields, leaving non-sensitive data unaffected
- Preserves application performance and overall data structure integrity
- Supports diverse protection needs for PII, PCI, PHI, or other confidential data
Centralized Policy Enforcement
Policies are centrally defined in the Enterprise Security Administrator (ESA) and enforced locally by the Application Protector at runtime—ensuring consistent protection in 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 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