LLM Application for Protegrity AI Developer Edition

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.

What You’ll See:

• Automated PII Redaction: See how the classification API identifies and redacts sensitive information like names and addresses in real-time.
• Semantic Guardrails: Learn how to detect and block malicious intent, including attempts to harvest admin credentials or execute system hacks.
• Input and Output Screening: Understand the importance of a two-way security gate that processes both user prompts and model responses.
• Log Protection: Learn how to prevent sensitive clear-text data from being captured in your system logs.

Video Chapters:
[0:00-1:30] - Architecture and LLM Security Objectives: Explore the dual-gate architecture designed to screen input and output, ensuring a secure perimeter around your Large Language Models.
[1:31-2:29] - Secure Customer Support Workflow: A walkthrough of a valid support request involving an address change and how the system handles legitimate user intent.
[2:30-3:51] - Real-Time PII Detection and Masking: See the engine in action as it identifies sensitive data and redacts it into generic entities before it ever reaches the LLM or system logs.
[3:52-4:35] - Defending Against Malicious Prompt Injection: Watch how the system reacts to "jailbreak" attempts and unauthorized requests for administrative credentials and system passwords.
[4:36-5:00] - Semantic Guardrails - Blocking Unauthorized Intent: A deep dive into why the guardrails trigger a hard block on malicious prompts, preventing any data transmission to the model.
[5:01-End] - Balancing Data Privacy and Utility: Final breakdown of how to allow helpful interactions, block malicious intent, and maintain PII redaction across AI/ML workflows.

About Protegrity AI Developer Edition

The Protegrity AI Developer Edition allows developers to integrate advanced data protection into AI/ML workflows. By combining entity detection with semantic analysis, organizations can safely utilize the power of LLMs while maintaining strict compliance and security standards.

Relevant Search Terms

LLM Security, Data Privacy in AI, PII Redaction for LLMs, Semantic Guardrails, Protegrity AI Developer Edition, AI Prompt Injection Prevention, Secure AI Workflows, Data Classification API, Machine Learning Security, Protecting Sensitive Data in Logs