AI Agent Guardrails Simplified - Prompt Injection, PII & More

Your AI agent doesn't need to be hacked to fail — sometimes a single sentence is enough. In this video, I break down the first of three guardrail layers every production AI agent needs, and show you exactly how to detect and block the most common input-side failures using Python. What's covered in this video: ✅ The 3-layer guardrail architecture every agent needs (input → output → tool-calling) ✅ Prompt injection — detecting attacks with an open-source ML classifier ✅ PII detection & masking — identifying and redacting sensitive data before it reaches the LLM ✅ Content moderation — flagging toxic/harmful input in real time ✅ Topic/scope restriction — keeping your agent inside its intended domain ✅ Full working Python notebook demos for every guardrail, using Groq + gpt-oss-120b Tools & libraries used: Microsoft Presidio (PII detection & anonymization) ProtectAI DeBERTa (prompt injection detection) Detoxify (content moderation) Zero-shot classification / LLM-based scope guardrails Groq API (gpt-oss-120b) Pydantic (structured validation) ⏱️ Timestamps: 00:00 Intro 01:18 Agent Guardrails 02:31 Mask Sensitive Data 05:24 Detect Prompt Injection 07:05 Content Safety 08:23 Scope Validation 10:33 What Next? 🧡 If this helped, a like and subscribe helps this channel a lot — new videos on applied AI engineering every week. #AIAgents #PromptInjection #LLMSecurity #AIEngineering #Python #GenAI #AgentGuardrails #aisecurity #agent #productionai