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Protect
Your Data
in AI Models
use in AI systems without slowing teams or breaking compliance.
Define Policy Once.
Enforce It Everywhere.
Traditional security was built to control infrastructure and identities.
AI requires security that governs data.
Protegrity decouples policy, enforcement, and cryptography to enable secure computation on protected data — enforcing governance in real time across on-prem, cloud, and SaaS.
Users — Apps — Models
- Compliance
- Quantum
- Sovereign Data
- Secure Agents
- Secure Inferencing
-
Control
Plane- Discovery & Classification
- Centralized, Agentic Policy
- Real-Time Audit & Observability
-
Execution
Plane- API & Endpoints
- Semantic Tokens & Guardrails
- Pseudonymization & Anonymization
-
Advanced
Cryptography- Vault & Vaultless
- Queryable Encryption
- Iceberg Encrypted Data Type
Data — Systems — Code
- Distributed Enforcement
- On-Prem, Cloud, SaaS
- Agent, Proxy, SDK
Secure, Production-Ready AI Pipelines
Most AI projects stall when sensitive data can’t be used safely in production workflows.
Protegrity removes the security, risk, and compliance barriers that slow progress or block AI deployments.
Query enterprise data using natural language without exposing sensitive fields — enabling analytics that remain accurate, explainable, and governed in production.
Orchestrate agents that retrieve data, reason across systems, and take action — while data stays protected and agent behavior remains auditable and production-ready.
Augment models with enterprise data without leaks or compliance violations — with secure, governed access, retrieval, and generation end to end.
Share data across teams and systems without duplication or exposure — with protection that travels with the data, enabling collaboration while maintaining control.
Drive Growth
Reduce Risk
ACHIEVE COMPLIANCE
THE LATEST
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