Protegrity, a global data security leader, has released its free Developer Edition on GitHub to help developers, data scientists, ML engineers and privacy/security engineers integrate powerful data protection into GenAI and unstructured data workflows without requiring enterprise infrastructure. The first enterprise-grade, governance-driven Python package, Protegrity Developer Edition is designed to enable developers to build secure and trustworthy data engineering workflows—including AI pipelines and data preparation—ensuring a well-governed and successful AI experience.

In a world where AI is outpacing policy and data drives both breakthroughs and breaches, privacy cannot be bolted on, it must be built in.

Protegrity Developer Edition removes common barriers to evaluation and experimentation with a lightweight, containerized deployment and intuitive Representational State Transfer (REST) and Python APIs. Developers can quickly discover and protect sensitive data, advancing Protegrity’s mission to make enterprise-grade data protection accessible.

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“We didn’t build this for the boardroom, we built it for the creators,” said Michael Howard, Chief Executive Officer, Protegrity. “Protegrity Developer Edition is our way of saying, ‘Go ahead, break things, test boundaries and protect data like it matters, because it does.’ In a world where AI is outpacing policy and data drives both breakthroughs and breaches, privacy cannot be bolted on, it must be built in. That’s why we’re putting powerful tools directly into developers’ hands, with no gatekeepers and no waiting, making security a first-class citizen.”

Built for Creators: Fast, Flexible, and GenAI-Ready

Protegrity Developer Edition includes data Discovery, sample applications, APIs and semantic guardrails.

  • Discovery: Identify sensitive data in logs, documents, and text using a combination of machine learning classifiers and pattern-based techniques such as regular expressions.
  • Find & Protect APIs: Let developers discover and protect sensitive data in minutes using REST or Python, spanning prompts, training data, RAG retrieval, and model outputs.
  • Semantic Guardrails: Modular, real-time defense layer that inspects inputs, plans, tool calls, and outputs to block prompt injection, PII leakage, and off-topic responses before they execute. Recent attacks that embed hidden instructions into seemingly benign content highlight why this proactive, context-aware protection is critical for securing GenAI pipelines.

Protegrity Developer Edition is tailored for privacy-critical GenAI use cases:

  • Privacy in conversational AI: Sensitive chatbot inputs such as names, emails and IDs are protected before they reach generative AI models.
  • Prompt sanitization for LLMs: Automated PII masking in prompts reduces risk during large language model prompt engineering and inference.
  • Experimentation with Jupyter notebooks: Data scientists can prototype protection and discovery workflows directly in Jupyter notebooks for agile experimentation.
  • Output redaction and leakage prevention: Detect and redact sensitive data in model outputs before returning them to end users.
  • Responsible AI training data anonymization: Sensitive fields in training datasets are redacted to support compliant and ethical AI development.

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Protegrity Developer Edition uses trusted technology to empower developers with the ability to run everything on their own computers and test privacy features without the need for special licenses or complex setups. Protections can be controlled through a built-in policy with preconfigured users and user roles that provides the ability to tokenize, encrypt, mask or pseudonymize with authorization depending on user’s access levels.

A Strategic Entry Point for the AI-Driven Enterprise.

Protegrity Developer Edition serves as a strategic on-ramp to Protegrity’s broader platform. Developers and security practitioners can rapidly iterate and test integration without enterprise IT teams.

Key benefits include:

  • Frictionless evaluation: No license, no sales calls, no infrastructure setup.
  • Developer autonomy: Empower teams to test, build, and validate independently.
  • Real-world protection: Enterprise-aligned semantic guardrails enforce policies on prompts, training data, RAG, and outputs.
  • Seamless scalability: Find & Protect APIs ensure an effortless upgrade.
  • Community engagement: Community discussion and peer support on GitHub.

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Source – businesswire

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