Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Are you struggling to extend data protection to modern web applications without the friction of custom SDKs? Zero-Trust architecture and AI governance are no longer optional—they are operational imperatives. The Protegrity Browser Protector is a Manifest V3 Chrome extension designed to solve the "last mile" of data security. It allows authorized users to protect, unprotect, and mask sensitive information (like SSNs, PII, and financial data) directly within any web browser—without modifying the underlying application code.

See how Protegrity AI Developer Edition helps protect sensitive data in AI agent workflows built with LangGraph. This demo shows how Protegrity can fit into modern AI development pipelines as both a preprocessor and postprocessor guardrail, helping teams discover, protect, tokenize, mask, and redact sensitive data before it reaches an LLM — and before responses leave the application. In this video, you’ll learn how developers can.

Einstein called quantum entanglement "spooky action at a distance," but it’s actually a fundamental lesson in how particles share identical information. While classical bits are limited to 0 or 1, quantum bits (qubits) exist in a combination of both until the moment they are measured. Understanding these complex data states is essential as we move toward a future of quantum-resistant security and advanced AI. At Protegrity, we are dedicated to protecting your most sensitive data across every stage of the evolving digital landscape.

See how Protegrity and Presidio help developers secure sensitive data in AI workflows. This demo shows how Protegrity AI Developer Edition helps teams discover, protect, mask, and redact sensitive data before it reaches AI models, applications, or analytics pipelines. You’ll learn how developers can.

Securing LLM Workflows with Protegrity AI Developer Edition Learn how to protect sensitive data and prevent malicious prompt injections in your AI applications. In this technical walkthrough, Dan Johnson, Software Engineer at Protegrity, demonstrates a dual-gate security architecture designed to safeguard Large Language Models. Discover how to implement a security gateway that sits between your users and your LLM. This demonstration covers the integration of semantic guardrails and classification APIs to ensure data privacy and system integrity.

Want to test Protegrity’s data protection features without any local installation? In this tutorial, Dan Johnson shows you how to make your first protect and unprotect API calls directly in your browser using our interactive Jupyter Notebook (Binder). This is the fastest way to see Protegrity’s Python SDK in action—authenticating, applying protection policies, and maintaining data utility in real-time.

Stop struggling with complex security setups and get straight to building with the Protegrity Developer Edition. Our demo series, hosted by Dan Johnson, shows you how to deploy a full, self-contained data protection environment on your local machine in under 15 minutes using GitHub and Docker. You will learn to master everything from PII discovery and automated redaction to advanced encryption and semantic guardrails for AI workflows.

In this installment of our AI Developer Edition Set-up series, Dan Johnson, a software engineer at Protegrity, introduces semantic guardrails. Learn how to protect your LLM and chatbot workflows from malicious prompts and insecure AI responses. As AI becomes central to enterprise operations, controlling the context of conversations is a major challenge. Semantic guardrails provide a safety layer that ensures your AI stays on topic and never leaks sensitive PII.

Dan Johnson, a software engineer at Protegrity, demonstrates how to use the Protegrity Developer Edition to identify and redact Personally Identifiable Information (PII) from unstructured text. Building on our installation guide, we walk through real-world use cases using the Python SDK and Core Edition to transform "useless" raw data into secure, usable information for your AI and ML workflows.

Move beyond outdated security models that focus on protecting data infrastructure rather than the data itself. By embedding protection that travels with the data, you create a deterministic environment where data knows its own purpose and enables innovation at scale. Visit Protegrity.com to learn more.