Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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Move beyond outdated security models that focus on protecting data infrastructure rather than the data itself.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.

In this video, Dan Johnson walks through the core data protection capabilities of the Protegrity AI Developer Edition. We transition from simple data discovery and redaction to more advanced workflows: protecting, unprotecting, encrypting, and decrypting sensitive PII (Personally Identifiable Information). What You’ll Learn: Timestamps: - Exploring the Help command and parameter options Resources.

We are at an inflection point where AI, compliance, and quantum are exposing the dangerous fragility of traditional "envelope" security. The organizations that thrive in the next decade will be those that set their data free by embedding deterministic protection directly into the data itself. The organizations that lead the next decade won't just have better AI, they'll have better data security. Visit Protegrity.com to learn more.

Why Your Data Security Model Is Outdated For over 20 years, we’ve focused on the Data Envelope—securing the perimeter, the cloud, and the network. But in a world of AI and rapid data sharing, protecting the envelope is not enough. In this video, James Rice (VP of Product Marketing at Protegrity) explains why traditional security has become the biggest bottleneck for modern innovation. Whether you are a security leader, a data architect, or a business innovator, understanding this paradigm shift is essential for the next decade of growth.

Most developers know the pain of setting up enterprise security software—hours of dependencies, complicated installs, and endless permissions that slow projects down before they even start. Protegrity Developer Edition is different. It installs the way developers work: lightweight, local, and fast. In this demo, Dan Johnson, a software engineer at Protegrity, walks through the complete setup process.