Protegrity provides a data‑centric security platform that protects sensitive information at the element level across the entire AI and analytics lifecycle. It uses vaultless tokenization, format‑preserving encryption, masking, and semantic guardrails to keep data usable for BI, ML, and generative AI while enforcing unified policies and compliance in notebooks, pipelines, data lakes, and SaaS tools.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying AI and analytics pipelines often expose sensitive structured and unstructured data to unauthorized access, leakage, and compliance violations because traditional security controls focus on infrastructure and identities rather than the data itself.
Solution
Protegrity delivers a data‑centric security platform that protects sensitive information at the element level across the entire AI lifecycle. The solution applies vaultless tokenization, format‑preserving encryption, masking, and semantic anonymization to keep data usable for analytics while preventing exposure. Unified policies are defined once and enforced inline in notebooks, CI/CD pipelines, data lakes, warehouses, and third‑party SaaS tools via containerized APIs. Built‑in semantic guardrails evaluate prompts, model inputs, and outputs in real time, scoring risk and blocking unsafe content. The platform also offers privacy‑enhancing synthetic data generation and automated discovery/classification of PII, PCI, PHI, and IP in both structured and unstructured streams. All controls integrate with major cloud and on‑premise stacks (Databricks, Snowflake, AWS, Azure, GCP) without requiring code rewrites, providing continuous compliance and auditability for AI workloads.
Target Audience
Primary customers are enterprise data teams, AI/ML developers, and security/compliance officers who need to secure sensitive data in analytics, machine‑learning, and generative‑AI pipelines across hybrid cloud environments.
Features
- Vaultless tokenization and format‑preserving encryption that retain data utility for BI, ML, and reporting
- Automated data discovery and classification of PII, PCI, PHI, and IP in real‑time data flows
- Semantic guardrails that score and block high‑risk prompts, training data, and model outputs
- Inline masking and tokenization APIs for notebooks, CI/CD pipelines, and streaming jobs
- Privacy‑enabled synthetic data generation that mirrors statistical properties of production data
- Unified policy engine with role/attribute‑based access, context‑aware controls, and centralized audit logs
- Container and API deployment model for seamless integration with Databricks, Snowflake, AWS, Azure, GCP, and major databases
- High‑throughput performance designed for batch and streaming workloads at enterprise scale