Key Features and Benefits

AI Developer Edition is purpose-built for fast, frictionless exploration of Protegrity’s core capabilities.

The following features make it ideal for prototyping and integration:

Platform Capabilities

AI Developer Edition provides a comprehensive set of platform capabilities that simplify how developers integrate data protection into their workflows. From containerized deployment to cross-language SDK support, each component is designed for rapid setup, minimal configuration, and seamless iteration.

• Modular, Containerized Architecture: AI Developer Edition runs on Docker, making it easy to test, isolate, and iterate.
• Lightweight: No orchestration overhead. Just deploy the container and use the sample application.
• Python Module: An open-source Python module providing APIs to protect, unprotect, and reprotect sensitive data in Python-based applications. It is available through PyPI for easy installation.
• Java Library: An open-source Java library providing APIs to protect, unprotect, and reprotect sensitive data in Java-based applications. It is distributed using Maven Central for easy integration.
• AI Developer Edition API Service: A service hosted by Protegrity that allows developers to interact with Protegrity’s protection and discovery services through intuitive endpoints. It supports protection and unprotection of sensitive data, enabling rapid prototyping and testing of data protection scenarios without needing full-scale infrastructure. Registration is required for this service. The credentials can be obtained for free.
• Sample Apps and Data: Jumpstart evaluation with ready-to-run sample apps that demonstrate real-world use cases. These use cases include finding sensitive data in unstructured text, finding and redacting, finding and protecting or unprotecting sensitive data, multi-turn conversations, and agent coordination patterns. Adjust behavior through shared/config.json.
• Cross-platform: Works on Linux, macOS, and Windows.

Core Data Protection Services

Protegrity AI Developer Edition offers features that help build AI services. These features range from identifying and protecting sensitive information to generating safe synthetic alternatives.

• Data Discovery: Identifies and classifies sensitive data using built-in and custom classifiers with confidence scoring. Discovers and redacts sensitive data in datasets for use in training GenAI models or sharing with third parties.
• Semantic Guardrails: A security guardrail engine for AI systems. Evaluates risks in GenAI systems such as chatbots, workflows, and agents through advanced semantic analytics and intent classification to detect potentially malicious messages. Provides message and conversation level risk scoring and PII scanning to prevent context poisoning and enforce governance in multi-agent systems.
• Synthetic Data: Analyzes a data set and generates data that mimics the properties of real data, such as data types, ranges, correlations, and distributions, without containing any actual personal information. Enables safe model training and end-to-end agent workflow testing.
• Anonymization: Replaces sensitive data with anonymized values to protect privacy while maintaining the utility of the data for analysis and model training.

Secure Data and AI Pipelines

AI Developer Edition enables end-to-end privacy across the AI lifecycle from data ingestion and model training to inference and output delivery. This ensures that sensitive information is protected at every stage of the pipeline.

• Privacy in conversational AI: Sensitive chatbot inputs are protected before they reach generative AI models.
• Prompt sanitization for LLMs: Automated PII masking reduces risk during large language model prompt engineering and inference.
• Experimentation with Jupyter notebooks: Data scientists can prototype directly in Jupyter notebooks for agile experimentation.
• Output redaction and leakage prevention: Detect and protect sensitive data in model outputs before returning them to end users.
• Privacy-enhanced AI training: Sensitive fields in training datasets are de-identified to support compliant and secure AI development.

Note: This product is continuously improving. The features and capabilities mentioned here are either already available or will be available shortly.