Protegrity Developer Edition enables developers, data scientists, ML engineers, and security teams an easy way to add data protection into GenAI and unstructured data workflows, without the need for enterprise setup. Billed as the first enterprise-grade, governance-focused Python package, it is built to help teams create secure, well-governed data pipelines and AI workflows from the ground up.
Protegrity Developer Edition removes common barriers to evaluation and experimentation with a lightweight, containerized deployment and intuitive Representational State Transfer (REST) and Python APIs. It 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 and 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.
“Developers are at the forefront of innovation, and they need tools that don’t slow them down,” said Tui Leauanae, Head of Developer Relations, Protegrity. “Our goal is to make data protection accessible, actionable and aligned with how teams build.”
The solution 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 GenAI 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.
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.
Protegrity Developer Edition is available now on GitHub and the Python module is also available through PyPI, complete with documentation, sample applications and community support. Developers can explore the repository, deploy locally and begin implementing privacy-first solutions within minutes.
