Semantic Guardrails for AI/ML - Protegrity AI Developer Edition

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.

What You’ll See:

• What are Semantic Guardrails? Understanding how they control both user prompts and LLM responses to prevent "off-topic" or malicious interactions.
• Risk Scoring: How Protegrity Developer Edition provides message-level and conversational-level risk scoring to evaluate the safety of an interaction.
• Live Demo: We walk through a JSON sample of a "malicious" prompt (requesting HR admin data) and see how the Protegrity engine automatically rejects the interaction.
• Explanation & Confidence: Interpreting the system's output, including rejection reasons and PII discovery within the AI's attempted response.

Key Topics:

• Preventing "prompt injection" or off-topic queries in support bots.
• Monitoring AI responses for accidental PII leakage.
• Integrating conversational risk scoring into your DNI workflows.

Ready to secure your AI? Watch the full video and visit Protegrity.com to learn more.
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