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New Protegrity Developer Edition Puts Data Protection in Every Workflow

By Protegrity
Sep 30, 2025

Summary

5 min
  • Launch update — build secure AI pipelines fast. Protegrity Developer Edition is now available with a lightweight, containerized Python toolkit and Find & Protect APIs for tokenization, masking, and discovery, so developers can prototype on real workflows in minutes and carry the same integrations into production without rewrites.
  • Guardrails that keep GenAI safe in real time. Semantic Guardrails scan prompts and outputs across chatbots, RAG, and agentic tools to detect PII exposure, prompt injection, and adversarial content; teams can block or redact by role, log for audit, and maintain accuracy and compliance for use cases like fraud detection, customer support, and credit scoring.

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 up enterprise infrastructure. The release emphasizes privacy built in rather than bolted on, with an updated repository link provided in the corrected announcement.

What’s Included

  • Discovery: Identify sensitive data in text, documents, and logs using ML classifiers and patterns.
  • Find & Protect APIs: REST and Python interfaces to tokenize, mask, and protect data across prompts, training sets, RAG, and outputs.
  • Semantic Guardrails 1.0: Modular, real-time inspection of inputs, plans, tool calls, and responses to mitigate prompt injection, PII leakage, and off-topic misuse.

Who It’s For

Developers, ML/MLOps engineers, data and security architects, and platform teams who need to prototype privacy controls quickly, validate policies in real workflows, and scale to the commercial platform without rewrites.

Key Benefits

  • Frictionless evaluation: No license or heavy infrastructure; runs locally via containers.
  • Developer autonomy: Build, test, and validate protections in minutes.
  • Real-world protection: Policy-aligned guardrails for prompts, training data, RAG retrieval, and outputs.
  • Seamless scalability: The same Find & Protect APIs carry forward to production deployments.
  • Community support: Documentation, examples, and discussion on GitHub and PyPI.

Quotes from the release:

“We didn’t build this for the boardroom, we built it for the creators… privacy cannot be bolted on, it must be built in.” — Michael Howard, CEO

“Our goal is to make data protection accessible, actionable, and aligned with how modern teams build.” — Tui Leauanae, Head of Developer Relations

Get Started

Note: This page summarizes information from a Protegrity press release for convenience. For the complete announcement, please refer to the official source below.